Apple Inc.

Sudeng Hu

Xiaosong Zhou

Dazhong Zhang

Hsi-Jung Wu

Peikang Song

Jae Hoon Kim

Xing Wen

Hang Yuan

Christopher M Garrido

Karthick Santhanam

Patrick Miauton

Chris Yoochang Chung

David L Biderman

Luciano Miguel Verger

Ming Jin

Kevin Arthur Robertson

Jingteng Xue

Joshua L Hare

Xiaoxiao Zheng

Bess C Chan

Sasanka T Vemuri

Jinbo Qiu

Peter Shiang

In an example method, a system receives a plurality of frames of a video, and generates a data structure representing the video and representing a plurality of temporal layers. Generating the data structure includes: (i) determining a plurality of quality levels for presenting the video, where each of the quality levels corresponds to a different respective sampling period for sampling the frames of the video, (ii) assigning, based on the sampling periods, each of the frames to a respective one of the temporal layers of the data structure, and (iii) indicating, in the data structure, one or more relationships between (a) at least one the frames assigned to at least one of the temporal layers of the data structure, and (b) at least another one of the frames assigned to at least another one of the temporal layers of the data structure. Further, the system outputs the data structure.

Encoding and decoding video content

Techniques are disclosed for coding and decoding video data using object recognition and object modeling as a basis of coding and error recovery. A video decoder may decode coded video data received from a channel. The video decoder may perform object recognition on decoded video data obtained therefrom, and, when an object is recognized in the decoded video data, the video decoder may generate a model representing the recognized object. It may store data representing the model locally. The video decoder may communicate the model data to an encoder, which may form a basis of error mitigation and recovery. The video decoder also may monitor deviation patterns in the object model and associated patterns in audio content; if/when video decoding is suspended due to operational errors, the video decoder may generate simulated video data by analyzing audio data received during the suspension period and developing video data from the data model and deviation(s) associated with patterns detected from the audio data.

Applications for decoder-side modeling of objects identified in decoded video data

Techniques presented herein provide an improved relay user experience and improved management of scarce computing and network resources as the number of relay endpoints increases. A sourcing endpoint device may generate a media feed, such as video and/or audio feed, representing contribution from a conference participant. The sourcing endpoint device may generate a priority value for the media feed, and the priority value may be transmitted to other members of the relay along with the input feed. Priority values of the different relay participants may be used by other devices, for example, intermediate servers or receiving endpoint devices, to manage aspects of the relay. For example, a relay server may prune streams from select endpoint devices based on relative priority values received from those devices. Alternatively, receiving endpoint devices may alter presentation of received feeds based on their associated priority values.

Media feed prioritization for multi-party conferencing

Systems and methods for coding a video to be overlaid by annotations are devised. A motion compensated predictive coding is employed, wherein coding parameters of video pixel blocks are determined based on the pixel blocks' relation to the annotations. A decoder decodes the video and annotates it based on metadata, obtained from the coder or other sources, describing the annotations' appearance and rendering mode.

Efficient coding of video data in the presence of video annotations

Techniques are disclosed for managing memory allocations when coding video data according to multiple codec configurations. According to these techniques, devices may negotiate parameters of a coding session that include parameters of a plurality of different codec configurations that may be used during the coding session. A device may estimate sizes of decoded picture buffers for each of the negotiated codec configurations and allocate in its memory a portion of memory sized according to a largest size of the estimated decoded picture buffers. Thereafter, the devices may exchange coded video data. The exchange may involve decoding coded data of reference pictures and storing the decoded reference pictures in the allocated memory. During the coding session, the devices may toggle among the different negotiated codec configurations. As they do, reallocations of memory may be avoided.

Dynamic video configurations

Techniques are disclosed for managing reference frames for gradual coder refresh (GDR) operation. A GDR frame may be partitioned into a plurality of units, at least one of which is coded by instantaneous decoder refresh (IDR) techniques and other(s) of which are coded by other techniques such as inter-coding. The coded GDR frame may be exchanged between an encoder and a decoder. The encoder and decoder both may decode the GDR frame. The encoder and decoder may store the IDR-coded portion of the GDR frame in a reference picture buffer in a modified frame that includes, for the other portion(s) of the GDR frame, replacement content instead of the content obtained by decoding. The modified reference frame are expected by bias prediction search operations performed on later frame toward selection of the IDR-coded content as opposed to the replacement content.

Gradual decoder refresh techniques with management of reference pictures

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San Jose, CA, United States of America

Sudeng Hu