FAST MULTI-RATE ENCODING FOR ADAPTIVE STREAMING USING MACHINE LEARNING

    公开(公告)号:WO2022120100A1

    公开(公告)日:2022-06-09

    申请号:PCT/US2021/061678

    申请日:2021-12-02

    Applicant: BITMOVIN, INC.

    Abstract: According to embodiments of the disclosure, fast multi-rate encoding may be performed using machine learning by encoding a lowest quality representation to determine encoding parameters, processing raw data of the video using a neural network to obtain an intermediate output comprising encoding features, augmenting the intermediate output with additional encoding features to form a final tensor, and processing the final tensor with another neural network to obtain a classification output comprising a split or not split decision for an image data block. The classification output may be used to encode a highest quality representation, and then other representations of the video.

    LIGHTWEIGHT TRANSCODING AT EDGE NODES
    3.
    发明申请

    公开(公告)号:WO2022093535A1

    公开(公告)日:2022-05-05

    申请号:PCT/US2021/054823

    申请日:2021-10-13

    Applicant: BITMOVIN, INC.

    Abstract: Disclosed are systems and methods for lightweight transcoding of video. A distributed computing system for lightweight transcoding includes an origin server and an edge node, the origin server having a memory and a processor and configured to receive an input video comprising a bitstream, encode the bitstream into a set of representations corresponding to a full bitrate ladder, generate encoding metadata for the set of representations, and provide a representation and encoding metadata for the set of representations to an edge node, the edge node having a memory and a processor and configured to transcode the bitstream, or segments thereof, into the set of representations, and to serve one or more of the representations to a client.

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