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