In various embodiments, a training application generates a trained perceptual quality model that estimates perceived video quality for reconstructed video. The training application computes a pixels-per-degree value based on a normalized viewing distance and a display resolution. The training application computes a set of feature values corresponding to a set of visual quality metrics based on a reconstructed video sequence, a source video sequence, and the pixels-per-degree value. The training application executes a machine learning algorithm on the first set of feature values to generate the trained perceptual quality model. The trained perceptual quality model maps a particular set of feature values corresponding to the set of visual quality metrics to a particular perceptual quality score.
In various embodiments, a quality inference application estimates perceived video quality for reconstructed video. The quality inference application computes a set of feature values corresponding to a set of visual quality metrics based on a reconstructed frame, a source frame, a display resolution, and a normalized viewing distance. The quality inference application executes a trained perceptual quality model on the set of feature values to generate a perceptual quality score that indicates a perceived visual quality level for the reconstructed frame. The quality inference application performs one or more operations associated with an encoding process based on the perceptual quality score.
H04N 19/154 - Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
H04N 19/136 - Incoming video signal characteristics or properties
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/182 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
H04N 19/184 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
The disclosed computer-implemented method includes accessing cluster hardware information that identifies at least two different types of storage media within a cluster and provides an indication of a respective amount of data throughput for each identified type of storage media. The method next includes accessing popularity information for digital content that is to be stored in the cluster. The popularity information indicates how often the digital content is predicted to be accessed over a specified future period of time. The method also includes allocating the digital content on the different types of storage media within the cluster according to the popularity information. Accordingly, digital content predicted to have higher popularity is placed on storage media types with higher throughput amounts, and digital content predicted to have lower popularity is placed on storage media types with lower throughput amounts. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/231 - Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers or prioritizing data for deletion
G06F 3/06 - Digital input from, or digital output to, record carriers
H04N 21/218 - Source of audio or video content, e.g. local disk arrays
The disclosed computer-implemented method includes accessing a hard drive to measure operational characteristics of the hard drive. The method next includes deriving hard drive health factors used to control the hard drive that are based on the measured operational characteristics. The derived hard drive health factors include an average per-seek time indicating an average amount of time the hard drive spends seeking specified data that is to be read and an average read speed indicating an average amount of time the hard drive spends reading the specified data. The method next includes determining, based on the hard drive health factors and the operational characteristics, an amount of load servicing capacity currently available at the hard drive, and then includes regulating the amount of load servicing performed by the hard drive according to the determined amount of available load servicing capacity. Various other methods, systems, and computer-readable media are also disclosed.
The disclosed computer-implemented method may include accessing a media item that includes multiple media item segments that are to be played back in a specific manner. The method may also include generating playgraphs for the media item, where the playgraphs define different playback paths between media item segments. The method may next include selecting a specific playgraph from the generated playgraphs, and then providing the selected playgraph to a playback device. Playback of the media item according to the selected playgraph may thereby provide a customized presentation of the media item. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/8549 - Creating video summaries, e.g. movie trailer
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/262 - Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission or generating play-lists
H04N 21/2668 - Creating a channel for a dedicated end-user group, e.g. by inserting targeted commercials into a video stream based on end-user profiles
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
A method includes receiving, with a computing system, data representing a video item into a buffer. The method further includes outputting the video item from the buffer to a display system. The method further includes determining that utilization of the buffer falls below a predetermined threshold. The method further includes, in response to determining that the utilization of the buffer falls below the predetermined threshold, determining that there is a specified rebuffering point within a predetermined time frame. The method further includes pausing with the computing system, the video item at the specified rebuffering point in response to determining that there is the specified rebuffering point within the predetermined time frame.
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
H04N 21/432 - Content retrieval operation from a local storage medium, e.g. hard-disk
H04N 21/433 - Content storage operation, e.g. storage operation in response to a pause request or caching operations
7.
DISPLAYING A SCENE TO A SUBJECT WHILE CAPTURING THE SUBJECTS ACTING PERFORMANCE USING MULTIPLE SENSORS
A system surrounds an area with a first set of display panels. A second set of display panels is positioned above the area, and a third set of display panels is positioned below the area. A subject is positioned within the area and may be on an omnidirectional treadmill within the area. A controller communicates content to the first set of display panels, the second set of display panels, and the third set of display panels that presents a multidimensional scene when displayed. A set of sensors capture sensor data of the subject within the area while content is displayed. One or more of the sensors may be coupled to a repositioning system that repositions sensors so the subject remains in a field of view of different sensors. From sensor data of the subject, a representation of the subject may be generated for insertion into other video content.
In various embodiments, a kernel uses a NIC to transmit encrypted data over TCP connections. The kernel causes the NIC to encrypt first data to generate a first transmission control protocol (TCP) packet in accordance with a hardware-based transport layer security (TLS) offload mode. The kernel computes a first re-transmission statistic in response to the first TCP packet failing to reach a first device over a first TCP connection; the kernel assigns the first TCP connection to a software-based TLS offload mode instead of the hardware-based TLS offload mode based on the first re-transmission statistic. Subsequently, the kernel encrypts second data to generate a second TCP packet in accordance with the software-based TLS offload mode, the kernel then causes the NIC to transmit the second TCP packet to the first device over the first TCP connection.
The disclosed computer-implemented method includes identifying a target networking device communicatively connected to a cluster of networked server instances. The networked server instances are configured to consume network telemetry messages. The method further includes applying a shared lock to the target networking device, so that only a single networked server instance has access to network telemetry messages produced by the target networking device. The method also includes replicating the network telemetry messages produced by the locked target networking device to other server instances in the cluster, and then diverting requests for network telemetry messages from the locked target networking device to another server instance in the cluster of networked server instances. This allows the other server instance in the cluster to service the diverted telemetry data requests for the locked target networking device using the replicated network telemetry messages. Various other methods, systems, and computer-readable media are also disclosed.
A computer-implemented method includes identifying static information associated with a computing device that is running a media playback application. The method also includes monitoring the media playback application during a playback session to identify dynamic information associated with the playback session. Still further, the method includes instantiating a trained machine learning (ML) model to determine, based on historical usage data associated with the media playback application, a likelihood that the media playback application will experience an out of memory kill. The trained ML model implements a graded window to indicate a scaled likelihood that an out of memory kill will occur within a specified timeframe. Then, according to the trained ML model's determination, the method generates a prediction that an out of memory kill will occur for the media playback application within the specified timeframe. Various other methods, systems, and computer-readable media are also disclosed.
The disclosed computer-implemented method may include accessing a pre-rendered multimedia item. The pre-rendered multimedia item may have branching logic associated with it, where the branching logic includes branching points that direct non-sequential playback of the pre-rendered multimedia item. The method may also include initializing playback of the pre-rendered multimedia item and accessing, at the branching points, various trigger conditions that direct playback order of different segments of the pre-rendered multimedia item. The method may then include updating, based on the trigger conditions, at least some portion of custom state data. The method may further include playing back the segments of the pre-rendered multimedia item according to the updated state data. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/472 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
The disclosed computer-implemented method may include receiving, from a client device, a request for multimedia content, where the request includes both a manifest request that includes client identification data and a license request that includes a license challenge. The method may further include validating the received request for multimedia content using the client identification data in the manifest request and generating a manifest response that includes an identification of a specified multimedia content stream that is to be provided to the client device. The method may also include acquiring at least one license in response to the license request, where the license includes a response to the license challenge having various content keys, and then providing the specified multimedia content stream, including the generated manifest response and the acquired license, to the client device. Various other methods, systems, and computer-readable media are also disclosed.
G10K 11/178 - Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
The disclosed computer-implemented method may include accessing an audio stream with heterogenous audio content; dividing the audio stream into a plurality of frames; generating a plurality of spectrogram patches, each spectrogram patch within the plurality of spectrogram patches being derived from a frame within the plurality of frames; and providing each spectrogram patch within the plurality of spectrogram patches as input to a convolutional neural network classifier and receiving, as output, a classification of music within a corresponding frame from within the plurality of frames. Various other methods, systems, and computer-readable media are also disclosed.
G10H 1/00 - ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE - Details of electrophonic musical instruments
14.
SYSTEMS AND METHODS FOR AUTOMATICALLY GENERATING SOUND EVENT SUBTITLES
The disclosed computer-implemented method may include systems and methods for automatically generating sound event subtitles for digital videos. For example, the systems and methods described herein can automatically generate subtitles for sound events within a digital video soundtrack that includes sounds other than speech. Additionally, the systems and methods described herein can automatically generate sound event subtitles as part of an automatic and comprehensive approach that generates subtitles for all sounds within a soundtrack of a digital video—thereby avoiding the need for any manual inputs as part of the subtitling process.
G10L 25/57 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for processing of video signals
G10L 25/81 - Detection of presence or absence of voice signals for discriminating voice from music
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
In various embodiments, a localized video encoding application encodes localized videos. In operation, the localized video encoding application computes a prediction frame based on a target frame of a localized video and at least a portion of a reference frame of a decoded original video. The localized video encoding application computes a residual frame based on the prediction frame and the target frame of the localized video. The localized video encoding application performs one or more encoding operations on the residual frame to generate a frame of an encoded localization layer. The frame of the encoded localization layer and at least one frame of an encoded original video are subsequently transmitted to another device for decoding.
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
16.
TECHNIQUES FOR STEERING NETWORK TRAFFIC TO REGIONS OF A CLOUD COMPUTING SYSTEM
In various embodiments, domain name system (DNS) servers are implemented on a content distribution network (CDN) infrastructure in order to facilitate centralized control of traffic steering. Each server appliance in the CDN infrastructure acts as both an authoritative DNS nameserver and a dynamic request proxy, and each such server appliance is assigned to one of multiple cloud computing system regions. The assignment of server appliances to cloud regions is based on latency measurements collected via client application probes and an optimization that minimizes an overall latency experienced by the client applications subject to constraints that the maximum traffic to each cloud region is less than a capacity constraint for that region, the maximum deviation of traffic to each cloud regions at any point in time is less than a given percentage, and the maximum deviation of traffic between direct and indirect paths is less than a given percentage.
H04L 47/2441 - Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
H04L 61/4511 - Network directories; Name-to-address mapping using standardised directory access protocols using domain name system [DNS]
H04L 67/101 - Server selection for load balancing based on network conditions
H04L 67/1036 - Load balancing of requests to servers for services different from user content provisioning, e.g. load balancing across domain name servers
In various embodiments, an in-session recommendation application generates recommendations for users during streaming sessions. The in-session recommendation application generates a set of feature values for a set of features associated with a trained machine learning model based on user interactions that have occurred via a graphical user interface (GUI) during a current streaming session. The in-session recommendation application executes the trained machine learning model on at least the set of feature values and a first feature value associated with a first item to generate a first score. The in-session recommendation application generates a recommendation based on the first score and at least a second score that is associated with both a second item and the user interactions. The in-session recommendation application displays the recommendation within the GUI.
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/482 - End-user interface for program selection
A computer-implemented method includes accessing a video frame that includes a plurality of pixels. The method also includes generating a spatial mask that identifies pixels whose likelihood of causing banding within the video frame meets at least a minimum threshold likelihood. The method also includes computing a local distribution for a region of the video frame that includes at least those pixels that were identified by the spatial mask to detect bands in the video frame. The method further includes determining that the detected bands in the video frame are at least a minimum threshold size. The method also includes applying dithering to those detected bands in the video frame that were determined to meet the minimum threshold size. Various other methods, systems, and computer-readable media are also disclosed.
H04N 19/86 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
G06V 10/75 - Image or video pattern matching; Proximity measures in feature spaces using context analysis; Selection of dictionaries
G09G 3/20 - Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix
19.
TECHNIQUES FOR ASSESSING UNCERTAINTY OF A PREDICTIVE MODEL
One or more embodiments include a computer-implemented method that includes receiving a data set generated by a machine learning model, wherein the data set comprises a plurality of data samples that are independent of each other, performing two or more fitting operations to fit the data set to a regularized maximum likelihood estimators (MLEs), determining a variance associated with the data set based on a derivative associated with the regularized MLEs, and performing one or more operations associated with the machine learning model based on the variance.
Various embodiments of the present disclosure relate to a computer-implemented method of receiving a header associated with an object, where the header includes a limit value that specifies a quantity of times the object is to be served from a cache device before revalidation, and a current count value that specifies a number of times that the object has been served since a most-recent revalidation or load, receiving a request for the object from a requesting device, and upon determining that the current count value is below the limit value, serving the object to the requesting device from the cache device, or upon determining that the current count value matches the limit value, transmitting a request for revalidating the object.
In various embodiments a computer-implemented method for managing use of a shared compression dictionary in a distributed database environment. The method includes determining that a given version of the shared compression dictionary should be designated as a current primary version of the shared compression dictionary. The method also includes receiving, from a client device, first write data compressed with a previous primary version of the shared compression dictionary and in response to receiving the first write data, transmitting, to the client device, the current primary version of the shared compression dictionary and an instruction to compress new write data with the current primary version of the shared compression dictionary. Additionally, the method includes receiving, from the client device, a second write data compressed with the current primary version of the shared compression dictionary and storing the second write data in a database.
A playback application is configured to dynamically generate topology for an interactive media title. The playback application obtains an initial topology and also collects various data associated with a user interacting with the feature. The playback application then modifies the initial topology, based on the collected data, to generate a dynamic topology tailored to the user. The dynamic topology describes the set of choices available to the user during playback as well as which options can be selected by the user when making a given choice. In addition, the playback application also selectively buffers different portions of the interactive media title, based on the collected data, in anticipation of the user selecting particular options for available choices.
H04N 21/8541 - Content authoring involving branching, e.g. to different story endings
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/475 - End-user interface for inputting end-user data, e.g. PIN [Personal Identification Number] or preference data
H04N 21/8545 - Content authoring for generating interactive applications
23.
TECHNIQUES FOR AUTOMATICALLY INFERRING INTENTS OF SEARCH QUERIES
In various embodiments, an intent-based query processing application processes search queries. The intent-based query processing application computes lexical similarity scores between a search query and a set of entities. The intent-based query processing application computes entity relevance scores based on the lexical similarity scores and user engagement scores associated with both the search query and the set of entities. The intent-based query processing application computes a first category relevance score associated with both the search query and a first category based on the entity relevance scores. The intent-based query processing application determines an intent associated with the search query based on the first category relevance score. The intent-based query processing application generates a response to the search query based on the intent.
A playback application is configured to analyze audio frames associated with transitions between segments within a media title to identify one or more portions of extraneous audio. The playback application is configured to analyze the one or more portions of extraneous audio and then determine which of the one or more corresponding audio frames should be dropped. In doing so, the playback application can analyze a topology associated with the media title to determine whether any specific portions of extraneous audio are to be played outside of a logical ordering of audio samples set forth in the topology. These specific portions of extraneous audio are preferentially removed.
H04N 21/43 - Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronizing decoder's clock; Client middleware
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
H04N 21/439 - Processing of audio elementary streams
G06V 20/40 - Scenes; Scene-specific elements in video content
The disclosed computer-implemented method includes determining that incoming media item requests are to be skewed from a random distribution among server nodes, using a random distribution algorithm, to a directed distribution among the server nodes. The method then includes identifying, in a loading assignment, which media items are to be loaded onto specific server nodes to produce the directed distribution of media item requests. The method next includes preloading the identified media items onto the server nodes according to the loading assignment and receiving media item requests for the preloaded media items. The method then includes routing the received media item requests to the server nodes using the random distribution algorithm, where the random distribution algorithm is skewed to the directed distribution based on the preloading of the media items according to the identified loading assignment. Various other methods, systems, and computer-readable media are also disclosed.
The disclosed computer-implemented method includes systems for optimizing color rendition in an LED volume virtual production stage. For example, the described systems optimize or correct color rendition by applying a series of color correction matrices to color pixel values within the virtual production stage and to final captured imagery filmed within the virtual production stage. The described systems generate the color correction matrices from four calibration images taken within the virtual production stage. Various other methods, systems, and computer-readable media are also disclosed.
The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/431 - Generation of visual interfaces; Content or additional data rendering
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
H04N 21/4728 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content for selecting a ROI [Region Of Interest], e.g. for requesting a higher resolution version of a selected region
H04N 21/485 - End-user interface for client configuration
G06V 20/40 - Scenes; Scene-specific elements in video content
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.
G11B 27/28 - Indexing; Addressing; Timing or synchronising; Measuring tape travel by using information detectable on the record carrier by using information signals recorded by the same method as the main recording
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
G06V 20/40 - Scenes; Scene-specific elements in video content
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
In various embodiments, a computer-implemented method comprises receiving, from a first endpoint device, a first event during a first time period, modifying an event log to include a record associated with the first event, causing a rollup queue to include a request to count a first count value associated with the first event, and generating, in a second time period subsequent to the first time period, a counter value associated with at least the first event based on the rollup queue and the event log.
The disclosed computer-implemented method includes applying transport protocol heuristics to selective acknowledgement (SACK) messages received at a network adapter from a network node. The transport protocol heuristics identify threshold values for operational functions that are performed when processing the SACK messages. The method further includes determining, by applying the transport protocol heuristics to the SACK messages received from the network node, that the threshold values for the transport protocol heuristics have been reached. In response to determining that the threshold values have been reached, the method includes identifying the network node as a security threat and taking remedial actions to mitigate the security threat. Various other methods, systems, and computer-readable media are also disclosed.
One embodiment of the present invention sets forth a technique for performing schema-driven data processing. The technique includes detecting a first change to a first producer schema for a first dataset produced by a first data processor. The technique also includes performing a compatibility check between the first change and a first consumer schema associated with processing of the first dataset by a second data processor, wherein the first consumer schema includes a set of fields required by the second data processor. The technique further includes modifying an operation of the second data processor based on a result of the compatibility check.
One embodiment of the present invention sets forth a technique for deblocking video frames. The technique includes determining a filter length associated with a boundary between a first block and a second block included in the same video frame. The technique also includes computing a parameter value that minimizes a sum of squares of second derivatives associated with samples from the first block and second block that are adjacent to the boundary. The technique further includes determining a plurality of filter values based on the parameter value and the filter length, and applying a filter having the filter length and the filter values to additional samples within the first and second blocks to generate two filtered blocks corresponding to the first and second blocks. The technique additionally comprises generating a second video frame that includes the two filtered blocks.
H04N 19/86 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
H04N 19/80 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/132 - Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
H04N 19/186 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
H04N 19/117 - Filters, e.g. for pre-processing or post-processing
One embodiment of the present invention sets forth a technique, which includes dividing an input image into a first partial image that stores a first subset of bits in each pixel of the input image and a second partial image that stores a second subset of bits that is disjoint from the first subset of bits in each pixel of the input image. The technique also includes modifying a first set of pixels in the first partial image to generate a first partial image processing result and modifying a second set of pixels in the second partial image to generate a second partial image processing result. The technique further includes generating a combined image processing result based on a combination of the first partial image processing result and the second partial image processing result.
In various embodiments a dialogue matching application performs speech recognition operations on an audio segment to generate a sequence of words. The dialogue matching application determines a first dialogue match between a first subsequence of words included in the sequence of words and a script line included in a set of script lines. The dialogue matching application determines a second dialogue match between a second subsequence of words included in the sequence of words and the script line. The dialogue matching application receives, via a graphical user interface (GUI), an event that corresponds to an interaction between a user and an interactive GUI element. The dialogue matching application extracts a portion of the audio segment from a session recording based on the event to generate an audio clip that corresponds to both the script line and either the first subsequence or words or the second subsequence of words.
The disclosed computer-implemented method may include (1) accessing a first media data object and a different, second media data object that, when played back, each render temporally sequenced content, (2) comparing first temporally sequenced content represented by the first media data object with second temporally sequenced content represented by the second media data object to identify a set of common temporal subsequences between the first media data object and the second media data object, (3) identifying a set of edits relative to the set of common temporal subsequences that describe a difference between the temporally sequenced content of the first media data object and the temporally sequenced content of the second media data object, and (4) executing a workflow relating to the first media data object and/or the second media data object based on the set of edits. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G11B 27/031 - Electronic editing of digitised analogue information signals, e.g. audio or video signals
In various embodiments, structured pages are dynamically generated based on user inputs. In response to a user input such as a query, a page generating engine ranks content items according to relevance to the user input in order to generate a list of the content items that is ordered based on the relevance. The page generating engine further maps the content items to collections of content items that can be displayed together in a page. Then, the page generating engine generates a structured page that includes a subset of the collections and associated content items that are assigned to collections within the subset of collections based on relevance and/or coherence criteria. Thereafter, the structured page is transmitted to a client device for display via user interface.
Techniques are disclosed for improving user experience of multimedia streaming over computer networks. More specifically, techniques presented herein reduce (or eliminate) latency in playback start time for streaming digital media content resulting from digital rights management (DRM) authorizations. A streaming media client (e.g., a browser, set-top box, mobile telephone or tablet “app”) may request a “fast-expiring” license for titles the streaming media client predicts a user is likely to begin streaming. A fast-expiring license is a DRM license (and associated decryption key) which is valid for only a very limited time after being used for playback. During the validity period of such a license, the client device requests a “normal” or “regular” license to continue accessing the title after the fast-expiring license expires.
G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
H04N 21/4405 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs involving video stream decryption
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/482 - End-user interface for program selection
H04N 21/6334 - Control signals issued by server directed to the network components or client directed to client for authorisation, e.g. by transmitting a key
H04N 21/8355 - Generation of protective data, e.g. certificates involving usage data, e.g. number of copies or viewings allowed
H04N 21/84 - Generation or processing of descriptive data, e.g. content descriptors
39.
SYSTEMS AND METHODS FOR PROVIDING OPTIMIZED TIME SCALES AND ACCURATE PRESENTATION TIME STAMPS
The disclosed computer-implemented method includes determining, for multiple different media items, a current time scale at which the media items are encoded for distribution, where at least two of the media items are encoded at different frame rates. The method then includes identifying, for the media items, a unified time scale that provides a constant frame interval for each of the media items. The method also includes changing at least one of the media items from the current time scale to the identified unified time scale to provide a constant frame interval for the changed media item(s). Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
40.
TECHNIQUES FOR COMPONENT-BASED IMAGE PREPROCESSING
In various embodiments, an image preprocessing application preprocesses images. To preprocess an image, the image preprocessing application executes a trained machine learning model on first data corresponding to both the image and a first set of components of a luma-chroma color space to generate first preprocessed data. The image preprocessing application executes at least a different trained machine learning model or a non-machine learning algorithm on second data corresponding to both the image and a second set of components of the luma-chroma color space to generate second preprocessed data. Subsequently, the image preprocessing application aggregates at least the first preprocessed data and the second preprocessed data to generate a preprocessed image.
In various embodiments, a content platform receives content management system (CMS) data associated with a web page. The content platform generates one or more page construction items based on the CMS data, wherein each page construction item indicates a structure of a different element included in the web page. The content platform also generates one or more page content items based on the CMS data, where each page content item indicates a content of a different element included in the web page. The content platform stores the one or more page construction items and the one or more page content items in association with the web page, wherein a content interface associated with the web page is generated for display based on the one or more page construction items and one or more page content items.
In various embodiments, a content platform receives, from a user device, a request for a web page. In response to receiving the request for the web page, the content platform retrieves one or more page construction items associated with the web page and one or more page content items associated with the web page, wherein each page construction item included in the one or more page construction items indicates a structure of a different element included in the web page and each page content item included in the one or more page content items indicates a content of a different element included in the web page. The content platform generates a content interface associated with the web page based on the one or more page construction items, the one or more page content items, and user data associated with a user of the user device.
Various embodiments of the present application set forth a computer-implemented method comprising determining a set of digital assets to transfer to a destination device, generating, from the set of digital assets, a corresponding set of chunks, where each chunk is a pre-defined size, for each chunk in the set of chunks, transmitting the chunk to a service node included in a set of service nodes, and verifying that the service node received the chunk, where the set of service nodes receives at least two chunks of the set of chunks in parallel, and after the set of service nodes send the at least two chunks in parallel to the destination device, verifying that the destination device received the set of chunks.
In various embodiments, a match cutting application automatically generates candidate match cuts. In some embodiments, the match cutting application detects shot sequences within one or more video sequences, deduplicates the shot sequences, computes an encoding of each shot sequence after the deduplication, and compares the encodings associated with pairs of shot sequences using a distance metric to identify pairs of shot sequences that can be cut together to generate candidate match cuts. In addition, the match cutting application can apply an approximation technique, such as an approximate nearest neighbor search technique, to identify intermediate pairs of shot sequences prior to comparing encodings associated with the intermediate pairs of shot sequences.
G06V 20/40 - Scenes; Scene-specific elements in video content
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
47.
TECHNIQUES FOR JOINTLY TRAINING A DOWNSCALER AND AN UPSCALER FOR VIDEO STREAMING
In various embodiments a training application trains convolutional neural networks (CNNs) to reduce reconstruction errors. The training application executes a first CNN on a source image having a first resolution to generate a downscaled image having a second resolution. The training application executes a second CNN on the downscaled image to generate a reconstructed image having the first resolution. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates a first learnable parameter value included in the first CNN based on the reconstruction error to generate at least a partially trained downscaling CNN. The training application updates a second learnable parameter included in the second CNN based on the reconstruction error to generate at least a partially trained upscaling CNN.
In various embodiments an endpoint application reconstructs downscaled videos. The endpoint application accesses metadata associated with a portion of a downscaled video that has a first resolution and was generated using a trained downscaling convolutional neural network (CNN). The endpoint application determines, based on the metadata, an upscaler that should be used when upscaling the portion of the downscaled video. The endpoint application executes the upscaler on the portion of the downscaled video to generate a portion of a reconstructed video that is accessible for playback and has a second resolution that is greater than the first resolution.
H04N 19/436 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
H04N 19/30 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
The disclosed computer-implemented method includes determining that incoming media item requests are to be skewed from a random distribution among server nodes, using a random distribution algorithm, to a directed distribution among the server nodes. The method then includes identifying, in a loading assignment, which media items are to be loaded onto specific server nodes to produce the directed distribution of media item requests. The method next includes preloading the identified media items onto the server nodes according to the loading assignment and receiving media item requests for the preloaded media items. The method then includes routing the received media item requests to the server nodes using the random distribution algorithm, where the random distribution algorithm is skewed to the directed distribution based on the preloading of the media items according to the identified loading assignment. Various other methods, systems, and computer-readable media are also disclosed.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 67/1001 - Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
The disclosed computer-implemented method includes determining that incoming media item requests are to be skewed from a random distribution among server nodes, using a random distribution algorithm, to a directed distribution among the server nodes. The method then includes identifying, in a loading assignment, which media items are to be loaded onto specific server nodes to produce the directed distribution of media item requests. The method next includes preloading the identified media items onto the server nodes according to the loading assignment and receiving media item requests for the preloaded media items. The method then includes routing the received media item requests to the server nodes using the random distribution algorithm, where the random distribution algorithm is skewed to the directed distribution based on the preloading of the media items according to the identified loading assignment. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/231 - Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers or prioritizing data for deletion
H04N 21/218 - Source of audio or video content, e.g. local disk arrays
In various embodiments, a media delivery application transmits encoded chunks of a media title to a playback application. In operation, the media delivery application receives, via a media channel, an encoded chunk request that has been transmitted over a TCP connection. The media delivery application also receives, via a side channel, a pacing specification that is associated with the encoded chunk request and has been transmitted over the TCP connection. As per the encoded chunk request, the media delivery application retrieves encoded chunk content. The media delivery application sets a parameter associated with the TCP connection equal to a parameter value based on the pacing specification. Subsequently, the media delivery application causes TCP segments corresponding to the encoded chunk content to be transmitted, via the media channel, over the TCP connection in accordance with the first parameter value.
One embodiment of the present invention sets forth a technique for optimizing data in a dataset. The technique includes determining, based on one or more attributes of a dataset, an optimization that is associated with at least one of a file encoding, a file size, and a sort column. The technique also includes identifying a plurality of candidate configurations associated with the dataset and corresponding to the optimization, and for each candidate configuration, generating a corresponding set of evaluation metrics associated with the first optimization. The technique further includes determining, based on the sets of evaluation metrics corresponding to the plurality of candidate configurations, a set of configurations in the plurality of candidate configurations to be applied to the dataset. Finally, the technique includes modifying the dataset based on the set of configurations.
G06F 16/21 - Design, administration or maintenance of databases
G06F 16/22 - Indexing; Data structures therefor; Storage structures
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
53.
VIRTUAL AND INDEX ASSEMBLY FOR CLOUD-BASED VIDEO PROCESSING
Various embodiments set forth a computer-implemented method for processing media files comprising receiving an index file corresponding to a source media file, wherein the index file indicates location information associated with a plurality of encoded portions of the source media file; retrieving one or more encoded portions included in the plurality of encoded portions from at least one storage device based on the index file; and generating at least part of an encoded version of the source media file based on the one or more encoded portions.
H04N 19/426 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements using memory downsizing methods
H04N 19/70 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
H04N 19/177 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
54.
SYSTEMS AND METHODS FOR SPLINE-BASED OBJECT TRACKING
The disclosed computer-implemented method may include (1) accessing a video portraying an object within a set of frames, (2) defining a subset of key frames within the video based on movement of the object across the set of frames, (3) generating, for each key frame within the subset of key frames, a spline outlining the object within the key frame, (4) receiving input to adjust, for a selected key frame within the subset of key frames, a corresponding spline, and (5) interpolating the adjusted spline with a spline in a sequentially proximate key frame to define the object in frames between the selected key frame and the sequentially proximate key frame. Various other methods, systems, and computer-readable media are also disclosed.
The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/4728 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content for selecting a ROI [Region Of Interest], e.g. for requesting a higher resolution version of a selected region
H04N 21/431 - Generation of visual interfaces; Content or additional data rendering
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
H04N 21/485 - End-user interface for client configuration
G06V 20/40 - Scenes; Scene-specific elements in video content
56.
Techniques for improving resource utilization in a microservices architecture via priority queues
In various embodiments, a flexible queue application allocates messages stored in priority queues to clients. In operation, the flexible queue application receives, from a client, a request to allocate a message from a priority queue. At least a first message and a second message are stored in the priority queue, and the priority of the first message is higher than the priority of the second message. The flexible queue application determines that the first message is pending but does not satisfy an allocation constraint. The flexible queue allocation then determines that the second message is pending and satisfies the allocation constraint. The flexible queue application allocates the second message to the client. Advantageously, because the flexible queue application can adapt the priority-based ordering of priority queues based on allocation constraints, the flexible queue application can efficiently enforce resource-related constraints when allocating messages from priority queues.
H04L 41/5054 - Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
H04L 47/52 - Queue scheduling by attributing bandwidth to queues
H04L 47/2466 - Traffic characterised by specific attributes, e.g. priority or QoS using signalling traffic
H04L 47/2475 - Traffic characterised by specific attributes, e.g. priority or QoS for supporting traffic characterised by the type of applications
One embodiment of the present invention sets forth a technique for encoding video frames. The technique includes performing one or more operations to generate a plurality of denoised video frames associated with a video sequence. The technique also includes determining a first set of motion vectors based on a first denoised frame included in the plurality of denoised video frames and a second denoised frame included in the plurality of denoised video frames, and determining a first residual between the second denoised frame and a prediction frame associated with the second denoised frame. The technique further includes performing one or more operations to generate an encoded video frame associated with the second denoised frame based on the first set of motion vectors, the first residual, and a first frame that is included in the video sequence and corresponds to the first denoised frame.
H04N 19/85 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
H04N 19/52 - Processing of motion vectors by encoding by predictive encoding
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/593 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/80 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
H04N 19/147 - Data rate or code amount at the encoder output according to rate distortion criteria
58.
Context-aware interface layer for remote applications
Various embodiments of the present application set forth a computer-implemented method comprising receiving, at an endpoint device, a user input associated with a first remote application running on a workstation instance associated with the user, determining, based on a context associated with the user input, a first asset associated with the user input, and causing the workstation instance to modify an asset file in a local file system of the workstation instance, wherein the asset file corresponds to at least a portion of the first asset.
H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
59.
Machine learning techniques for component-based image preprocessing
In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.
H04N 19/86 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
H04N 19/186 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
H04N 19/30 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
H04N 19/132 - Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
G06N 3/04 - Architecture, e.g. interconnection topology
H04N 19/89 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving methods or arrangements for detection of transmission errors at the decoder
60.
Systems and methods for network telemetry data replication
The disclosed computer-implemented method includes identifying a target networking device communicatively connected to a cluster of networked server instances. The networked server instances are configured to consume network telemetry messages. The method further includes applying a shared lock to the target networking device, so that only a single networked server instance has access to network telemetry messages produced by the target networking device. The method also includes replicating the network telemetry messages produced by the locked target networking device to other server instances in the cluster, and then diverting requests for network telemetry messages from the locked target networking device to another server instance in the cluster of networked server instances. This allows the other server instance in the cluster to service the diverted telemetry data requests for the locked target networking device using the replicated network telemetry messages. Various other methods, systems, and computer-readable media are also disclosed.
One embodiment sets forth a technique that includes receiving a request for a range of data included in an encoded version of the media title that is stored across a set of files. The technique also includes determining, based on a file extent index, one or more files included in the set of files, wherein the file extent index maps an identifier for each file in the set to a given range of data that is stored in the file and included in the encoded version. The technique further includes retrieving the range of data from the file(s), wherein at least a portion of the retrieved range of data falls within the given range of data that is stored in each of the one or more files, and transmitting the range of data in a response to the request.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/239 - Interfacing the upstream path of the transmission network, e.g. prioritizing client requests
H04N 21/437 - Interfacing the upstream path of the transmission network, e.g. for transmitting client requests to a VOD server
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
62.
Systems and methods for providing optimized time scales and accurate presentation time stamps
The disclosed computer-implemented method includes determining, for multiple different media items, a current time scale at which the media items are encoded for distribution, where at least two of the media items are encoded at different frame rates. The method then includes identifying, for the media items, a unified time scale that provides a constant frame interval for each of the media items. The method also includes changing at least one of the media items from the current time scale to the identified unified time scale to provide a constant frame interval for the changed media item(s). Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
The disclosed method includes accessing video content encoded at a specified frame rate, and determining a refresh rate for an electronic display on which the video content is to be presented. The method next includes specifying a time interval for the video content over which frame rate conversion is to be applied to synchronize the video content frame rate with the electronic display refresh rate. The method also includes presenting the video content on the electronic display where the playback speed is adjusted for a first part of the interval. At this adjusted speed, the interval is played back using original video frames and multiple frame duplications. The presenting also adjusts playback speed of a second part of the interval. At the adjusted speed, the interval is played back using the original frames and a different number of frame duplications. Various other methods, systems, and computer-readable media are also disclosed.
In various embodiments, a data optimization application mitigates scoring inaccuracies in subjective quality experiments. In operation, the data optimization application generates a model that includes a first set of individual scores and a first set of parameters. The first set of parameters includes a first subjective score set and a first set of subjective factor sets. The data optimization application performs one or more optimization operations on the first set of parameters to generate a second set of parameters. The second set of parameters includes a second subjective score set and a second set of subjective factor sets, wherein the second subjective score set compensates for at least a first subjective factor set included in the second set of subjective factor sets. The data optimization application also computes a participant evaluation report based on at least a second subjective factor sets included in the second set of subjective factor sets
In some examples, a system includes a display wall comprising an arrangement of light-emissive elements and a controller, configured to receive camera data and to adjust a light emission profile of the arrangement of light-emissive elements based on the camera data. In some embodiments, the camera data may include camera location data. Various other methods, systems, and computer-readable media are also disclosed.
G09G 3/32 - Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix using controlled light sources using electroluminescent panels semiconductive, e.g. using light-emitting diodes [LED]
A method includes receiving, with a computing system, data representing a video item into a buffer. The method further includes outputting the video item from the buffer to a display system. The method further includes determining that utilization of the buffer falls below a predetermined threshold. The method further includes, in response to determining that the utilization of the buffer falls below the predetermined threshold, determining that there is a specified rebuffering point within a predetermined time frame. The method further includes pausing with the computing system, the video item at the specified rebuffering point in response to determining that there is the specified rebuffering point within the predetermined time frame.
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
H04N 21/432 - Content retrieval operation from a local storage medium, e.g. hard-disk
H04N 21/433 - Content storage operation, e.g. storage operation in response to a pause request or caching operations
H04N 21/61 - Network physical structure; Signal processing
68.
Methods and systems for providing dynamically composed personalized media assets
The disclosed computer-implemented method may include accessing a media item that includes multiple media item segments that are to be played back in a specific manner. The method may also include generating playgraphs for the media item, where the playgraphs define different playback paths between media item segments. The method may next include selecting a specific playgraph from the generated playgraphs, and then providing the selected playgraph to a playback device. Playback of the media item according to the selected playgraph may thereby provide a customized presentation of the media item. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/8549 - Creating video summaries, e.g. movie trailer
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/262 - Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission or generating play-lists
H04N 21/2668 - Creating a channel for a dedicated end-user group, e.g. by inserting targeted commercials into a video stream based on end-user profiles
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
The disclosed computer-implemented method may include accessing updated data structures that are to be included in a user interface functionality test, where the updated data structures contribute to a user interface. The method may also include accessing live or snapshotted data captured from services running in a production environment, initiating generation of a first user interface instance using the updated data structures and using the accessed live or snapshotted data, and initiating generation of a second user interface instance using a different version of the data structures and using the same accessed live or snapshotted data. The method further includes comparing the first user interface instance to the second user interface instance to identify differences and then determine which outcome-defining effects the updated data structures had on the user interface based on the identified differences between the user interfaces. Various other methods, systems, and computer-readable media are also disclosed.
A system surrounds an area with a first set of display panels. A second set of display panels is positioned above the area, and a third set of display panels is positioned below the area. A subject is positioned within the area and may be on an omnidirectional treadmill within the area. A controller communicates content to the first set of display panels, the second set of display panels, and the third set of display panels that presents a multidimensional scene when displayed. A set of sensors capture sensor data of the subject within the area while content is displayed. One or more of the sensors may be coupled to a repositioning system that repositions sensors so the subject remains in a field of view of different sensors. From sensor data of the subject, a representation of the subject may be generated for insertion into other video content.
The disclosed computer-implemented method includes identifying a target networking device communicatively connected to a cluster of networked server instances. The networked server instances are configured to consume network telemetry messages. The method further includes applying a shared lock to the target networking device, so that only a single networked server instance has access to network telemetry messages produced by the target networking device. The method also includes replicating the network telemetry messages produced by the locked target networking device to other server instances in the cluster, and then diverting requests for network telemetry messages from the locked target networking device to another server instance in the cluster of networked server instances. This allows the other server instance in the cluster to service the diverted telemetry data requests for the locked target networking device using the replicated network telemetry messages. Various other methods, systems, and computer-readable media are also disclosed.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 67/563 - Data redirection of data network streams
H04L 67/1031 - Controlling of the operation of servers by a load balancer, e.g. adding or removing servers that serve requests
H04L 67/1095 - Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
72.
MANAGEMENT OF A SCALABLE POOL OF WORKSTATION INSTANCES
Various embodiments of the present application set forth a computer-implemented method comprising receiving, from a client, a request for a workstation instance having a first configuration, in response to the request, generating a first workstation pool associated with the first configuration, wherein the first workstation pool includes at least two unassigned workstation instances having the first configuration, and assigning at least a first workstation instance included in the at least two unassigned workstation instances to the client.
A playback application is configured to analyze audio frames associated with transitions between segments within a media title to identify one or more portions of extraneous audio. The playback application is configured to analyze the one or more portions of extraneous audio and then determine which of the one or more corresponding audio frames should be dropped. In doing so, the playback application can analyze a topology associated with the media title to determine whether any specific portions of extraneous audio are to be played outside of a logical ordering of audio samples set forth in the topology. These specific portions of extraneous audio are preferentially removed.
H04N 21/43 - Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronizing decoder's clock; Client middleware
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
H04N 21/439 - Processing of audio elementary streams
G06V 20/40 - Scenes; Scene-specific elements in video content
74.
TECHNIQUES FOR AUTOMATICALLY EXTRACTING COMPELLING PORTIONS OF A MEDIA CONTENT ITEM
In various embodiments, a clip application computes a set of appearance values for an appearance metric based on shot sequences associated with a media content item. Each appearance value in the set of appearance values indicates a prevalence of a first character in a different shot sequence associated with the media content item. The clip application then performs one or more clustering operations on the shot sequences based on the set of appearance values to generate a first shot cluster. Subsequently, the clip application generates a clip for the media content item based on the first shot cluster. The clip application transmits the clip to an endpoint device for display. Advantageously, relative to primarily manual approaches, the clip application can more efficiently and reliably generate clips for media content items.
Various embodiments set forth systems and techniques for concurrent log and dump processing. The techniques include selecting, from a datastore, a chunk comprising one or more rows of data; comparing the one or more rows of data in the chunk with a first set of log events in a change log associated with the datastore, wherein each log event included in the first set of log events occurs after a first log event in the change log and prior to a second log event in the change log; selecting, based on the comparison, one or more non-conflicting rows in the chunk; and transmitting, to an output, one or more log events associated with the one or more non-conflicting rows in the chunk prior to processing a second set of log events in the change log, wherein the second set of log events occur after the second log event.
One embodiment sets forth a technique that includes receiving a request for a range of data included in an encoded version of the media title that is stored across a set of files. The technique also includes determining, based on a file extent index, one or more files included in the set of files, wherein the file extent index maps an identifier for each file in the set to a given range of data that is stored in the file and included in the encoded version. The technique further includes retrieving the range of data from the file(s), wherein at least a portion of the retrieved range of data falls within the given range of data that is stored in each of the one or more files, and transmitting the range of data in a response to the request.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/437 - Interfacing the upstream path of the transmission network, e.g. for transmitting client requests to a VOD server
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
H04N 21/239 - Interfacing the upstream path of the transmission network, e.g. prioritizing client requests
The disclosed computer-implemented method includes accessing cluster hardware information that identifies at least two different types of storage media within a cluster and provides an indication of a respective amount of data throughput for each identified type of storage media. The method next includes accessing popularity information for digital content that is to be stored in the cluster. The popularity information indicates how often the digital content is predicted to be accessed over a specified future period of time. The method also includes allocating the digital content on the different types of storage media within the cluster according to the popularity information. Accordingly, digital content predicted to have higher popularity is placed on storage media types with higher throughput amounts, and digital content predicted to have lower popularity is placed on storage media types with lower throughput amounts. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/231 - Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers or prioritizing data for deletion
G06F 3/06 - Digital input from, or digital output to, record carriers
H04N 21/218 - Source of audio or video content, e.g. local disk arrays
In various embodiments, an encoding ladder application generates encoding ladders for encoding media titles. In operation, the encoding ladder application generates a first convex hull representing encoding tradeoffs between quality and bitrate when encoding a media title at a first resolution; The encoding ladder application generates a second convex hull representing encoding tradeoffs between quality and bitrate when encoding the media title at a second resolution. Based on the first convex hull and the second convex hull, the encoding ladder application generates an overall convex hull. Subsequently, the encoding ladder application generates an encoding ladder for the media title based on at least the overall convex hull and a ladder requirement. Advantageously, the tradeoffs between quality and bitrate represented by the encoding ladder are customized for the media title. Consequently, encoding inefficiencies attributable to conventional fixed-bitrate ladders can be reduced.
H04N 19/146 - Data rate or code amount at the encoder output
H04N 19/154 - Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
H04N 19/184 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
H04N 19/30 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
G11B 27/28 - Indexing; Addressing; Timing or synchronising; Measuring tape travel by using information detectable on the record carrier by using information signals recorded by the same method as the main recording
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
G06V 20/40 - Scenes; Scene-specific elements in video content
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
A banding detection application generates a first set of pixel confidence values based on a first intensity difference value and first image scale associated with a first image, wherein each pixel confidence value included in the first set of pixel confidence values indicates a likelihood that a corresponding pixel included in the first image at the first image scale corresponds to banding in the first image. The banding detection application then generates a banding index corresponding to the first image based on the first set of pixel confidence values.
H04N 19/86 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
H04N 19/182 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
H04N 19/117 - Filters, e.g. for pre-processing or post-processing
81.
Systems and methods for optimizing hard drive throughput
The disclosed computer-implemented method includes accessing a hard drive to measure operational characteristics of the hard drive. The method next includes deriving hard drive health factors used to control the hard drive that are based on the measured operational characteristics. The derived hard drive health factors include an average per-seek time indicating an average amount of time the hard drive spends seeking specified data that is to be read and an average read speed indicating an average amount of time the hard drive spends reading the specified data. The method next includes determining, based on the hard drive health factors and the operational characteristics, an amount of load servicing capacity currently available at the hard drive, and then includes regulating the amount of load servicing performed by the hard drive according to the determined amount of available load servicing capacity. Various other methods, systems, and computer-readable media are also disclosed.
The disclosed computer-implemented method may include predicting, by a machine learning model, a user action and a timing of the user action for an application on a computing device. The method may also include determining that an expected lag in executing the user action is greater than a predetermined threshold based on one or more resource constraints of the computing device and initializing a setup of the application to reduce the expected lag in executing the user action based on the predicted timing. Furthermore, the method may include prefetching one or more application components in response to initializing the setup of the application and preprocessing at least a portion of a function of the application used to execute the user action. Finally, the method may include executing the user action for the application in response to a user request. Various other methods, systems, and computer-readable media are also disclosed.
In various embodiments, a quality of experience (QoE) prediction application computes a visual quality score associated with a stream of encoded video content. The QoE prediction application also determines a rebuffering duration associated with the stream of encoded video content. Subsequently, the QoE prediction application computes an overall QoE score associated with the stream of encoded video content based on the visual quality score, the rebuffering duration, and an exponential QoE model. The exponential QoE model is generated using a plurality of subjective QoE scores and a linear regression model. The overall QoE score indicates a quality level of a user experience when viewing reconstructed video content derived from the stream of encoded video content.
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/658 - Transmission by the client directed to the server
H04N 21/4425 - Monitoring of client processing errors or hardware failure
H04N 21/6547 - Transmission by server directed to the client comprising parameters, e.g. for client setup
H04N 21/475 - End-user interface for inputting end-user data, e.g. PIN [Personal Identification Number] or preference data
84.
Dynamically cropping digital content for display in any aspect ratio
One embodiment of the present invention sets forth a technique for dynamically cropping image data transmitted to an endpoint device. The technique includes computing a first visual interest score for a first visual interest region within a digital image based on content included in the first visual interest region, computing a second visual interest score for a second visual interest region within the digital image based on content included in the second visual interest region, and determining that the first visual interest region is preferred over the second visual interest region based on the first visual interest score and the second visual interest score. The technique further includes setting a location within the first visual interest region as a point of visual interest and transmitting the digital image and the location of the point of visual interest to a computing device for displaying a portion of the digital image that includes the point of visual interest.
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network. Advantageously, the trained convolution neural network can be implemented in a video encoding pipeline to mitigate visual quality reductions typically experienced with conventional video encoding pipelines that implement conventional downsampling techniques.
In various embodiments, a monitoring application assesses delays associated with a circuit within a network. The monitoring application determines a measured trip time between a first device and a second device that is connected to the first device via the circuit. The measured trip time is associated with a first variance attributable to the first device. The monitoring application performs one or more digital signal processing operations based on the measured trip time to generate a predicted trip time. The predicted trip time is associated with a second variance attributable to the first device that is less than the first variance. Based on the predicted trip time, the monitoring application determines characteristic(s) of the delay associated with the circuit. Advantageously, reducing variations attributable to the first device when generating the first predicted trip time increases the accuracy with which the monitoring application can determine the characteristic(s) of the delay.
Various embodiments set forth systems and techniques for securing media content capture capabilities on a device. The techniques include receiving a frame of a media content item; determining whether the frame of the media content item is signed based on an analysis of one or more pixels of the frame; and when the media content is signed, removing one or more restrictions on one or more functions on the device based on whether the one or more pixels on the frame meet one or more conditions, wherein the one or more functions enable the device to perform one or more operations on the frame of the media content item.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
H04N 1/32 - Circuits or arrangements for control or supervision between transmitter and receiver
G06F 21/76 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in application-specific integrated circuits [ASIC] or field-programmable devices, e.g. field-programmable gate arrays [FPGA] or programmable logic devices [PLD]
G06F 21/74 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information operating in dual or compartmented mode, i.e. at least one secure mode
G06F 21/16 - Program or content traceability, e.g. by watermarking
88.
MACHINE LEARNING TECHNIQUES FOR GENERATING ENJOYMENT SIGNALS FOR WEIGHTING TRAINING DATA
Various embodiments set forth systems and techniques for training a personalized prediction model. The techniques include generating, based on interaction data associated with one or more users and a first weight associated with the interaction data, a first set of training data; generating, based on the personalized prediction model, a predicted enjoyment signal associated with playback of a digital content item; generating, based on the first set of training data and the predicted enjoyment signal, a second set of training data; and updating one or more parameters of a personalized ranking model based on the second set of training data.
A playback application seamlessly advances playback of and interactive media title in response to user selections in a manner that minimizes latency and preserves user immersion in a narrative. The playback application buffers an interstitial segment included in the interactive media title and feeds portions of the interstitial segment to a media player only when those portions are needed for display. When the user selects an option displayed during the interstitial segment, the playback application begins buffering a subsequent media segment and stops feeding portions of the interstitial segment to the media player. The playback application starts feeding blocks of the subsequent media segment to the media player and then seamlessly advances playback to the subsequent media segment.
In various embodiments, an encoder comparison application compares the performance of different configured encoders. In operation, the encoder comparison application generates a first global convex hull of video encode points based on a first configured encoder and a set of subsequences included in a source video sequence. Each video encode point is associated with a different encoded version of the source video sequence. The encoder comparison application also generates a second global convex hull of video encode points based on a second configured encoder and the subsequences. Subsequently, the encoder configuration application computes a performance value for an encoding comparison metric based on the first global convex hull and the second global convex hull. Notably, the first performance value estimates a difference in performance between the first configured encoder and the second configured encoder.
H04N 19/196 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters
H04N 19/85 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
91.
Techniques for steering network traffic to regions of a cloud computing system
In various embodiments, domain name system (DNS) servers are implemented on a content distribution network (CDN) infrastructure in order to facilitate centralized control of traffic steering. Each server appliance in the CDN infrastructure acts as both an authoritative DNS nameserver and a dynamic request proxy, and each such server appliance is assigned to one of multiple cloud computing system regions. The assignment of server appliances to cloud regions is based on latency measurements collected via client application probes and an optimization that minimizes an overall latency experienced by the client applications subject to constraints that the maximum traffic to each cloud region is less than a capacity constraint for that region, the maximum deviation of traffic to each cloud regions at any point in time is less than a given percentage, and the maximum deviation of traffic between direct and indirect paths is less than a given percentage.
H04L 47/125 - Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
H04L 47/2441 - Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
G06F 17/11 - Complex mathematical operations for solving equations
H04N 21/239 - Interfacing the upstream path of the transmission network, e.g. prioritizing client requests
H04L 67/1036 - Load balancing of requests to servers for services different from user content provisioning, e.g. load balancing across domain name servers
In various embodiments, a subsequence-based encoding application generates subsequences based on a source sequence associated with a media title. The subsequence-based encoding application then encodes both a first subsequence and a second subsequence across each of multiple configured encoders and at least one rate control value to generate, respectively, a first set of encoded subsequences and a second set of encoded subsequences. Notably, each configured encoder is associated with a combination of an encoder and a configuration, and at least two configured encoders are different from one another. Subsequently, the subsequence-based encoding application generates encoded media sequences based on the first set of encoded subsequences and the second set of encoded subsequences. Finally, the application selects a first encoded media sequence from the encoded media sequences based on a first target value for a media metric to subsequently stream to a first endpoint device during playback of the media title.
H04N 21/2662 - Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/238 - Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
93.
FIRE-AND-FORGET OFFLOAD MECHANISM FOR NETWORK-BASED SERVICES
In various embodiments, a proxy application processes requests associated with a network-based service. In operation, the proxy application determines that a first request received from a client application indicates that a response to the first request can be offloaded from a server machine. Prior to transmitting the first request to the server machine, the proxy application transmits a response to the first request to the client application. The response indicates that the server machine has successfully processed the first request. Advantageously, upon receiving the response, the client application is able to initiate a second request irrespective of the server machine.
In various embodiments, servers in a server pool share a centrally-generated encryption key that is used to encrypt session keys in tickets that can be used by client applications to share secure communication sessions across different servers belonging to the same server pools. When the client application first establishes a secure communication session with a server in a server pool, the server returns a ticket that includes an encrypted version of a session key generated using an encryption key shared by servers in the server pool. Thereafter, the client application can present the ticket to other servers in the server pool to restore the secure communication session with the other servers. Associations between servers and server pools can be communicated to the client application in any technically feasible manner, such as part of information indicating servers from which content can be accessed or via the Domain Name System (DNS).
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
H04L 29/06 - Communication control; Communication processing characterised by a protocol
In various embodiments, a templated Domain Name System (DNS) nameserver respond to DNS to DNS requests, and a templated HyperText Transfer Protocol (HTTP) server respond to HTTP and Hypertext Transfer Protocol Secure (HTTPS) requests. The templated DNS nameserver responds to a DNS request with a predefined response specifying an Internet Protocol (IP) address associated with the templated HTTP server, based on a type of the DNS request. The templated HTTP server responds to an HTTP or HTTPS request with a redirection response specifying a default redirection Universal Resource Locator (URL), or a different URL if the default has been overridden. Domain management is simplified relative to conventional approaches using the templated DNS nameserver and the templated HTTP server. A new domain can simply be registered at a domain name registrar, and the templated DNS nameserver configured as an authoritative DNS nameserver at the domain name registrar.
In various embodiments, a tunable VMAF application reduces an amount of influence that image enhancement operations have on perceptual video quality estimates. In operation, the tunable VMAF application computes a first value for a first visual quality metric based on reconstructed video content and a first enhancement gain limit. The tunable VMAF application computes a second value for a second visual quality metric based on the reconstructed video content and a second enhancement gain limit. Subsequently, the tunable VMAF application generates a feature value vector based on the first value for the first visual quality metric and the second value for the second visual quality metric. The tunable VMAF application executes a VMAF model based on the feature value vector to generate a tuned VMAF score that accounts, at least in part, for at least one image enhancement operation used to generate the reconstructed video content.
H04N 19/86 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
The disclosed computer-implemented method may include accessing a pre-rendered multimedia item. The pre-rendered multimedia item may have branching logic associated with it, where the branching logic includes branching points that direct non-sequential playback of the pre-rendered multimedia item. The method may also include initializing playback of the pre-rendered multimedia item and accessing, at the branching points, various trigger conditions that direct playback order of different segments of the pre-rendered multimedia item. The method may then include updating, based on the trigger conditions, at least some portion of custom state data. The method may further include playing back the segments of the pre-rendered multimedia item according to the updated state data. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/472 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
In various embodiments, a subsequence-based encoding application generates a convex hull of subsequence encode points based on multiple encoding points and a first subsequence included in a set of subsequences that are associated with a media title. The subsequence-based encoding application then generates a first encode list that includes multiple subsequence encode points based on the first convex hull. Notably, each subsequence encode point included in the first encode list is associated with a different subsequence. The subsequence-based encoding application selects a first subsequence encode point included in the first encode list based on a first variability constraint that is associated with a media metric. The subsequence-based encoding application then replaces the first subsequence encode point included in the first encode list with a second subsequence encode point to generate a second encode list. Finally, the subsequence-based encoding application generates an encoded media sequence based on the second encode list.
H04N 19/154 - Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/59 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
H04N 19/87 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving scene cut or scene change detection in combination with video compression
H04N 21/238 - Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
H04N 21/2662 - Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
The disclosed computer-implemented method may include accessing a pre-rendered multimedia item. The pre-rendered multimedia item may have branching logic associated with it, where the branching logic includes branching points that direct non-sequential playback of the pre-rendered multimedia item. The method may also include initializing playback of the pre-rendered multimedia item and accessing, at the branching points, various trigger conditions that direct playback order of different segments of the pre-rendered multimedia item. The method may then include updating, based on the trigger conditions, at least some portion of custom state data. The method may further include playing back the segments of the pre-rendered multimedia item according to the updated state data. Various other methods, systems, and computer-readable media are also disclosed.
H04N 21/472 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
In various embodiments, an encoding optimization application encodes a source video sequence. The encoding optimization application partitions a source video sequence associated with a media title into multiple subsequences that include a first subsequence and a second subsequence. The encoding optimization application then generates a first encoded subsequence based on the first subsequence and a first resolution. The encoding optimization application generates a second encoded subsequence based on the second subsequence and a second resolution. Notably, the second resolution is higher than the first resolution. The encoding optimization application then aggregates the first encoded subsequence and the second encoded subsequence to generate an encoded video sequence. At least a portion of the encoded video sequence is subsequently streamed to an endpoint device during a playback of the media title.
H04N 19/147 - Data rate or code amount at the encoder output according to rate distortion criteria
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/192 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding the adaptation method, adaptation tool or adaptation type being iterative or recursive
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/238 - Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
H04N 19/179 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a scene or a shot
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
H04L 29/06 - Communication control; Communication processing characterised by a protocol
H04N 19/177 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
G11B 20/00 - Signal processing not specific to the method of recording or reproducing; Circuits therefor
G11B 27/30 - Indexing; Addressing; Timing or synchronising; Measuring tape travel by using information detectable on the record carrier by using information signals recorded by the same method as the main recording on the same track as the main recording
H04N 19/196 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters
H04N 19/59 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
patents
