Image Decoding Method, Image Encoding Method, and Apparatus

    公开(公告)号:US20250037317A1

    公开(公告)日:2025-01-30

    申请号:US18914881

    申请日:2024-10-14

    Abstract: A method includes that a decoder side processes, based on a group of feature domain optical flows corresponding to an image frame, a first feature map of a reference frame to obtain a group of intermediate feature maps. The decoder side fuses the group of intermediate feature maps to obtain a predicted feature map, and the decoder side decodes the image frame based on the predicted feature map to obtain a target image. The predicted feature map of the image frame is determined by the decoder side by fusing a plurality of intermediate feature maps, and the predicted feature map includes more image information.

    Encoding Method, Decoding Method, and Electronic Device

    公开(公告)号:US20240422332A1

    公开(公告)日:2024-12-19

    申请号:US18820582

    申请日:2024-08-30

    Abstract: An encoding method includes obtaining a to-be-encoded frame, where the to-be-encoded frame is a P-frame, determining, from M preset network parameter sets, a network parameter set corresponding to the to-be-encoded frame, where the M preset network parameter sets respectively correspond to different compression performance information, and M is an integer greater than one, and encoding, by an encoding network, and based on the network parameter set corresponding to the to-be-encoded frame, the to-be-encoded frame to obtain a bitstream representative of the to-be-encoded frame.

    Video Frame Compression Method, Video Frame Decompression Method, and Apparatus

    公开(公告)号:US20230281881A1

    公开(公告)日:2023-09-07

    申请号:US18316750

    申请日:2023-05-12

    CPC classification number: G06T9/002

    Abstract: A video frame compression method includes determining a target neural network from a plurality of neural networks according to a network selection policy; and generating, by using the target neural network, compression information corresponding to a current video frame. If the compression information is obtained by using a first neural network, the compression information includes first compression information of a first feature of the current video frame, and a reference frame of the current video frame is used for a compression process of the first feature of the current video frame. If the compression information is obtained by using a second neural network, the compression information includes second compression information of a second feature of the current video frame, and a reference frame of the current video frame is used for a generation process of the second feature of the current video frame.

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