  The traditional image compressors, e.g., BPG and H.266, have achieved great
image and video compression quality. Recently, Convolutional Neural Network has
been used widely in image compression. We proposed an attention-based
convolutional neural network for low bit-rate compression to post-process the
output of traditional image compression decoder. Across the experimental
results on validation sets, the post-processing module trained by MAE and
MS-SSIM losses yields the highest PSNR of 32.10 on average at the bit-rate of
0.15.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning