  In learning-based approaches to image compression, codecs are developed by
optimizing a computational model to minimize a rate-distortion objective.
Currently, the most effective learned image codecs take the form of an
entropy-constrained autoencoder with an entropy model that uses both forward
and backward adaptation. Forward adaptation makes use of side information and
can be efficiently integrated into a deep neural network. In contrast, backward
adaptation typically makes predictions based on the causal context of each
symbol, which requires serial processing that prevents efficient GPU / TPU
utilization. We introduce two enhancements, channel-conditioning and latent
residual prediction, that lead to network architectures with better
rate-distortion performance than existing context-adaptive models while
minimizing serial processing. Empirically, we see an average rate savings of
6.7% on the Kodak image set and 11.4% on the Tecnick image set compared to a
context-adaptive baseline model. At low bit rates, where the improvements are
most effective, our model saves up to 18% over the baseline and outperforms
hand-engineered codecs like BPG by up to 25%.
