Abstract: A video delivery system generates and stores reduced bandwidth videos from source video. The system may include a track generator that executes functionality of application(s) to be used at sink devices, in which the track generator generates tracks from execution of the application(s) on source video and generates tracks having a reduced data size as compared to the source video. The track generator may execute a first instance of application functionality on the source video, which identifies region(s) of interest from the source video. The track generator further may downsample the source video according to downsampling parameters, and execute a second instance of application functionality on the downsampled video. The track generator may determine, from a comparison of outputs from the first and second instances of the application, whether the output from the second instance of application functionality is within an error tolerance of the output from the first instance of application functionality.

Abstract: Techniques are disclosed for generating virtual reference frames that may be used for prediction of input video frames. The virtual reference frames may be derived from already-coded reference frames and thereby incur reduced signaling overhead. Moreover, signaling of virtual reference frames may be avoided until an encoder selects the virtual reference frame as a prediction reference for a current frame. In this manner, the techniques proposed herein contribute to improved coding efficiencies.

Abstract: Techniques are disclosed for coding video data in which frames from a video source are partitioned into a plurality of tiles of common size, and the tiles are coded as a virtual video sequence according to motion-compensated prediction, each tile treated as having respective temporal location of the virtual video sequence. The coding scheme permits relative allocation of coding resources to tiles that are likely to have greater significance in a video coding session, which may lead to certain tiles that have low complexity or low motion content to be skipped during coding of the tiles for select source frames. Moreover, coding of the tiles may be ordered to achieve low coding latencies during a coding session.

Abstract: An encoder or decoder can perform enhanced motion vector prediction by receiving an input block of data for encoding or decoding and accessing stored motion information for at least one other block of data. Based on the stored motion information, the encoder or decoder can generate a list of one or more motion vector predictor candidates for the input block in accordance with an adaptive list construction order. The encoder or decoder can predict a motion vector for the input block based on at least one of the one or more motion vector predictor candidates.

Abstract: Techniques are disclosed for coding video data in which frames from a video source are partitioned into a plurality of tiles of common size, and the tiles are coded as a virtual video sequence according to motion-compensated prediction, each tile treated as having respective temporal location of the virtual video sequence. The coding scheme permits relative allocation of coding resources to tiles that are likely to have greater significance in a video coding session, which may lead to certain tiles that have low complexity or low motion content to be skipped during coding of the tiles for select source frames. Moreover, coding of the tiles may be ordered to achieve low coding latencies during a coding session.

Abstract: Systems and processes for improved video editing, summarization and navigation based on generation and analysis of metadata are described. The metadata may be content-based (e.g., differences between neighboring frames, exposure data, key frame identification data, motion data, or face detection data) or non-content-based (e.g., exposure, focus, location, time) and used to prioritize and/or classify portions of video. The metadata may be generated at the time of image capture or during post-processing. Prioritization information, such as a score for various portions of the image data may be based on the metadata and/or image data. Classification information such as the type or quality of a scene may be determined based on the metadata and/or image data. The classification and prioritization information may be metadata and may be used to automatically remove undesirable portions of the video, generate suggestions during editing or automatically generate summary video.

Abstract: Techniques for cropping images containing an occlusion are presented. A method for image editing is presented comprising, when an occlusion is detected in an original digital image, determining an area occupied by the occlusion, assigning importance scores to different content elements of the original digital image, defining a cropping window around an area of the original digital image that does not include the area occupied by the occlusion and that is based on the importance scores, and cropping the original digital image to the cropping window.

Abstract: A method for processing media assets includes, given a first media asset, deriving characteristics from the first media asset, searching for other media assets having characteristics that correlate to the characteristics of the first media asset, when a match is found, deriving content corrections for the first media asset or a matching media asset from the other of the first media asset or the matching media asset, and correcting content of the first media asset or the matching media asset based on the content corrections.

Abstract: Methods for organizing media data by automatically segmenting media data into hierarchical layers of scenes are described. The media data may include metadata and content having still image, video or audio data. The metadata may be content-based (e.g., differences between neighboring frames, exposure data, key frame identification data, motion data, or face detection data) or non-content-based (e.g., exposure, focus, location, time) and used to prioritize and/or classify portions of video. The metadata may be generated at the time of image capture or during post-processing. Prioritization information, such as a score for various portions of the image data may be based on the metadata and/or image data. Classification information such as the type or quality of a scene may be determined based on the metadata and/or image data. The classification and prioritization information may be metadata and may be used to organize the media data.

Abstract: Techniques for cropping images containing an occlusion are presented. A method for image editing is presented comprising, when an occlusion is detected in an original digital image, determining an area occupied by the occlusion, assigning importance scores to different content elements of the original digital image, defining a cropping window around an area of the original digital image that does not include the area occupied by the occlusion and that is based on the importance scores, and cropping the original digital image to the cropping window.

Abstract: A method for processing media assets includes, given a first media asset, deriving characteristics from the first media asset, searching for other media assets having characteristics that correlate to the characteristics of the first media asset, when a match is found, deriving content corrections for the first media asset or a matching media asset from the other of the first media asset or the matching media asset, and correcting content of the first media asset or the matching media asset based on the content corrections.

Abstract: Methods for organizing media data by automatically segmenting media data into hierarchical layers of scenes are described. The media data may include metadata and content having still image, video or audio data. The metadata may be content-based (e.g., differences between neighboring frames, exposure data, key frame identification data, motion data, or face detection data) or non-content-based (e.g., exposure, focus, location, time) and used to prioritize and/or classify portions of video. The metadata may be generated at the time of image capture or during post-processing. Prioritization information, such as a score for various portions of the image data may be based on the metadata and/or image data. Classification information such as the type or quality of a scene may be determined based on the metadata and/or image data. The classification and prioritization information may be metadata and may be used to organize the media data.

Abstract: Systems and processes for improved video editing, summarization and navigation based on generation and analysis of metadata are described. The metadata may be content-based (e.g., differences between neighboring frames, exposure data, key frame identification data, motion data, or face detection data) or non-content-based (e.g., exposure, focus, location, time) and used to prioritize and/or classify portions of video. The metadata may be generated at the time of image capture or during post-processing. Prioritization information, such as a score for various portions of the image data may be based on the metadata and/or image data. Classification information such as the type or quality of a scene may be determined based on the metadata and/or image data. The classification and prioritization information may be metadata and may be used to automatically remove undesirable portions of the video, generate suggestions during editing or automatically generate summary video.

Abstract: The invention is directed to an efficient way for encoding and decoding video. Embodiments include identifying different coding units that share a similar characteristic. The characteristic can be, for example: quantization values, modes, block sizes, color space, motion vectors, depth, facial and non-facial regions, and filter values. An encoder may then group the units together as a coherence group. An encoder may similarly create a table or other data structure of the coding units. An encoder may then extract the commonly repeating characteristic or attribute from the coding units. The encoder may transmit the coherence groups along with the data structure, and other coding units which were not part of a coherence group. The decoder may receive the data, and utilize the shared characteristic by storing locally in cache, for faster repeated decoding, and decode the coherence group together.

Abstract: The invention is directed to an efficient way for encoding and decoding video. Embodiments include identifying different coding units that share a similar characteristic. The characteristic can be, for example: quantization values, modes, block sizes, color space, motion vectors, depth, facial and non-facial regions, and filter values. An encoder may then group the units together as a coherence group. An encoder may similarly create a table or other data structure of the coding units. An encoder may then extract the commonly repeating characteristic or attribute from the coding units. The encoder may transmit the coherence groups along with the data structure, and other coding units which were not part of a coherence group. The decoder may receive the data, and utilize the shared characteristic by storing locally in cache, for faster repeated decoding, and decode the coherence group together.