BLOCK-BASED LONG-RANGE CONTEXT MODEL IN NEURAL IMAGE COMPRESSION

    公开(公告)号:US20240214592A1

    公开(公告)日:2024-06-27

    申请号:US18458507

    申请日:2023-08-30

    CPC classification number: H04N19/42 H04N19/119 H04N19/167 H04N19/176

    Abstract: Methods and apparatuses for decoding a compressed image using a neural image compression network may be provided. The method may include generating long-range context model parameters associated with a high resolution compressed image, the long-range context model parameters corresponding to a first area. The method may also include splitting the generated long-range context model parameters into a first number of context parameter blocks. The method may also include for each block in the first number of context parameter blocks, predicting respective context features using a long-range context model and respective context parameter blocks, wherein the long-range context model uses a corner-to-center latent decoding strategy or an edge-to-center latent decoding strategy to decode latents associated with the high resolution compressed image. Then, the high resolution compressed image may be reconstructed based on predicted context features.

    LONG-RANGE CONTEXT MODEL IN NEURAL IMAGE COMPRESSION

    公开(公告)号:US20240212215A1

    公开(公告)日:2024-06-27

    申请号:US18458595

    申请日:2023-08-30

    CPC classification number: G06T9/00 G06T7/13 G06V10/44 G06T2207/20164

    Abstract: Methods and apparatuses for decoding a compressed image using a neural image compression network are provided. The method may include generating context parameters associated with a compressed image, the context parameters corresponding to a first area. The method may also include determining that a long range global dependency exists between a first latent and a second latent in a long-range global area within the compressed image and predicting a plurality of context features using a transformer-based long-range context prediction model. The method may then include reconstructing the compressed image based on the predicted plurality of context features.

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