Machine learning based rate-distortion optimizer for video compression

    公开(公告)号:US11496746B2

    公开(公告)日:2022-11-08

    申请号:US17165680

    申请日:2021-02-02

    Abstract: Systems and techniques are described for data encoding using a machine learning approach to generate a distortion prediction {circumflex over (D)} and a predicted bit rate {circumflex over (R)}, and to use {circumflex over (D)} and {circumflex over (R)} to perform rate-distortion optimization (RDO). For example, a video encoder can generate the distortion prediction {circumflex over (D)} and the bit rate residual prediction based on outputs of the one or more neural networks in response to the one or more neural networks receiving a residual portion of a block of a video frame as input. The video encoder can determine bit rate metadata prediction based on metadata associated with a mode of compression, and determine {circumflex over (R)} to be the sum of and . The video encoder can determine a rate-distortion cost prediction Ĵ as a function of {circumflex over (D)} and {circumflex over (R)}, and can determine a prediction mode for compressing the block based on Ĵ.

    IMAGE REMOSAICING
    4.
    发明申请
    IMAGE REMOSAICING 审中-公开

    公开(公告)号:US20190139189A1

    公开(公告)日:2019-05-09

    申请号:US15804898

    申请日:2017-11-06

    Abstract: Image processing can include producing a first mosaic of an image with a first spectral pattern, assigning a context to the first mosaic, classifying the first mosaic based on the assigned context, and producing a second mosaic of the image based on the classifying. The second mosaic can have a second spectral pattern different than the first spectral pattern.

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