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We propose a method for learned compression artifact removal bypost-processing of BPG compressed images. We trained three networks ofdifferent sizes. We encoded input images using BPG with different QP values. Wesubmitted the best combination of test images, encoded with different QP andpost-processed by one of three networks, which satisfy the file size and decodetime constraints imposed by the Challenge. The selection of the bestcombination is posed as an integer programming problem. Although the visualimprovements in image quality is impressive, the average PSNR improvement forthe results is about 0.5 dB.

Learned Compression Artifact Removal by Deep Residual Networks