Qualcomm Incorporated

Google Inc.

Akshay Gadde

Marta Karczewicz

Vadim Seregin

Wei-Jung Chien

Li Feng Zhang

Paul Gordon Wilkins

Amir Said

Nan Hu

Hilmi Enes Egilmez

Balineedu Adsumilli

Cheng Che Chen

Sam John

Ching Yin Derek Pang

Kyrah Felder

A learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. Complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. The rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. This approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.

Multivariate rate control for transcoding video content

Methods and systems for encoder parameter setting optimization. A media item to be provided to one or more users of a platform is identified. The media item is associated with a media class. An indication of the identified media item is provided as input to a first machine learning model. The first machine learning model is trained to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item. One or more outputs of the first machine learning model are obtained. The one or more obtained outputs include encoder data identifying one or more sets of encoder parameter settings and, for each of the sets of encoder parameter settings, an indication of a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item. The identified media item is encoded using the respective set of encoding parameter settings associated with the level of confidence that satisfies a confidence criterion.

Methods and systems for encoder parameter setting optimization

A video encoder and video decoder may determine a set of adaptive loop filters, from among a plurality of sets of adaptive loop filters, on a per-block basis. Each set of adaptive loop filters may include filters from a previous picture, filters signaled for the current picture, and/or pre-trained filter. By varying the set of adaptive loop filters on a per-block basis, the adaptive loop filters available for each block of video data may be more adapted to local statistics of the video data.

Block-based adaptive loop filter (ALF) design and signaling

Techniques are described for improving transform coding. For example, an encoded block of video data can be obtained, and a width and/or a height of the block can be determined. The width can be compared to a first threshold and/or the height can be compared to a second threshold. A horizontal transform and a vertical transform can be determined for the block based on comparing the width of the block to the first threshold and/or the height of the block to the second threshold. The horizontal transform and the vertical transform are determined without decoding a syntax element that indicates the horizontal transform and the vertical transform (e.g., the syntax element is not in an encoded video bitstream processed by a decoding device). In some cases, residual data is determined using the horizontal and vertical transforms, and a video block is determined using the residual data and a predictive block.

Adaptive multiple transform coding

A video decoder can be configured to set each block-level syntax element of a plurality of block-level syntax elements to a value indicating that an adaptive loop filter is enabled for an associated component of the video data in response to determining that a slice-level syntax element is set to a value indicating that values for the plurality of block-level syntax elements are inferred.

Block-based adaptive loop filter design and signaling

Codecs that use larger blocks may have larger boundary regions that may benefit from filtering. In some embodiments, the deblocking filter determines filters and/or a number of samples of the block to be filtered based on block dimensions. For example, In one embodiment, deblocking filter parameters for the video block are determined based on at least one dimension of the size of the video block. The filter parameters include a filter to be applied or a number of pixels along a boundary with a neighboring block to which the filter is to be applied determined based on the at least one dimension.

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Fremont, CA, United States of America

Akshay Gadde