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In learning-based approaches to image compression, codecs are developed byoptimizing a computational model to minimize a rate-distortion objective.Currently, the most effective learned image codecs take the form of anentropy-constrained autoencoder with an entropy model that uses both forwardand backward adaptation. Forward adaptation makes use of side information andcan be efficiently integrated into a deep neural network. In contrast, backwardadaptation typically makes predictions based on the causal context of eachsymbol, which requires serial processing that prevents efficient GPU / TPUutilization. We introduce two enhancements, channel-conditioning and latentresidual prediction, that lead to network architectures with betterrate-distortion performance than existing context-adaptive models whileminimizing serial processing. Empirically, we see an average rate savings of6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to acontext-adaptive baseline model. At low bit rates, where the improvements aremost effective, our model saves up to 18% over the baseline and outperformshand-engineered codecs like BPG by up to 25%.

Channel-wise Autoregressive Entropy Models for Learned Image Compression