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Found 3 papers, 1 papers with code
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Then we only encode non-zero channels in the encoding and decoding process, which can greatly reduce the encoding and decoding time.
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance.
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image.
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