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We leverage the powerful lossy image compression algorithm BPG to build alossless image compression system. Specifically, the original image is firstdecomposed into the lossy reconstruction obtained after compressing it with BPGand the corresponding residual. We then model the distribution of the residualwith a convolutional neural network-based probabilistic model that isconditioned on the BPG reconstruction, and combine it with entropy coding tolosslessly encode the residual. Finally, the image is stored using theconcatenation of the bitstreams produced by BPG and the learned residual coder.The resulting compression system achieves state-of-the-art performance inlearned lossless full-resolution image compression, outperforming previouslearned approaches as well as PNG, WebP, and JPEG2000.

Learning Better Lossless Compression Using Lossy Compression