Channel-wise Autoregressive Entropy Models for Learned Image Compression

Understand

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.