Microsoft Technology Licensing, LLC

Beijing Bytedance Network Technology Co., Ltd.

Bytedance Inc.

Jiahao Li

Jian Li

Jizheng Xu

Ming-Chieh Lee

Yan Lu

Li Feng Zhang

Kai Zhang

Hongbin Liu

Yue Wang

Weijia Zhu

Andrey Mikhaylovic Mezentsev

Thomas W Holcomb

Mei-Hsuan Lu

Satya Sasikanth Bendapudi

Jizhe Jin

Shao-Ting Wang

Tom W Holcomb

Innovations in intra-picture prediction with multiple candidate reference lines available are described herein. For example, intra-picture prediction for a current block uses a non-adjacent reference line of sample values to predict the sample values of the current block. This can improve the effectiveness of the intra-picture prediction when the reference line of sample values that is adjacent the current block includes significant capture noise, significant quantization error, or significantly different values (compared to the current block) due to an occlusion. Innovations described herein include, but are not limited to, the following: intra-picture prediction with multiple candidate reference lines available; encoding/decoding of reference line indices using prediction; filtering of reference sample values; residue compensation; weighted prediction; mode-dependent padding to replace unavailable reference sample values; using in-loop-filtered reference sample values; encoder-side decisions for selecting reference lines; and post-filtering of predicted sample values.

Intra-picture prediction using non-adjacent reference lines of sample values

Implementations of the subject matter described herein provide a solution for rate control based on reinforcement learning. In this solution, an encoding state of a video encoder is determined, the encoding state being associated with encoding of a first video unit by the video encoder. An encoding parameter associated with rate control in the video encoder is determined by a reinforcement learning model and based on the encoding state of the video encoder. A second video unit different from the first video unit is encoded based on the encoding parameter. In this way, it is possible to achieve a better quality of experience (QOE) for real time communication with computation overhead being reduced.

Reinforcement learning based rate control

Innovations in adaptive encoding of screen content based on motion type are described. For example, a video encoder system receives a current picture of a video sequence. The video encoder system determines a current motion type for the video sequence and, based at least in part on the current motion type, sets one or more encoding parameters. Then, the video encoder system encodes the current picture according to the encoding parameter(s). The innovations can be used in real-time encoding scenarios when encoding screen content for a screen sharing application, desktop conferencing application, or other application. In some cases, the innovations allow a video encoder system to adapt compression to different characteristics of screen content at different times within the same video sequence.

Adaptive encoding of screen content based on motion type

Methods, systems and device for hash-based motion estimation in video coding are described. An exemplary method of video processing includes determining, for a conversion between a current block of a video and a bitstream representation of the video, motion information associated with the current block using a hash-based motion search, a size of the current block being M&#xd7;N, M and N being positive integers and M being not equal to N, applying, based on the motion information and a video picture comprising the current block, a prediction for the current block, and performing, based on the prediction, the conversion.

Early determination of hash-based motion searching

Techniques are described for efficiently encoding video data by skipping evaluation of certain encoding modes based on various evaluation criteria. In some solutions, intra-block evaluation is performed in a specific order during encoding, and depending on encoding cost calculations of potential intra-block encoding modes, evaluation of some of the potential modes can be skipped. In some solutions, some encoding modes can be skipped depending on whether blocks are simple (e.g., simple vertical, simple horizontal, or both) or non-simple. In some solutions, various criteria are applied to determine whether chroma-from-luma mode evaluation can be skipped. The various solutions can be used independently and/or in combination.

Determination of coding modes for video content using order of potential coding modes and block classification

Simplification of hash-based motion searching

Hash-based motion searching

Screen content encoding mode evaluation including intra-block evaluation of multiple potential encoding modes

Growing community of inventors

Beijing, China

Jiahao Li