Microsoft Technology Licensing, LLC

Andrey Mikhaylovic Mezentsev

Jian Li

Ming-Chieh Lee

Yan Lu

Jiahao Li

Thomas W Holcomb

Mei-Hsuan Lu

Tom W Holcomb

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

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

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

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Redmond, WA, United States of America

Andrey Mikhaylovic Mezentsev