Edge-aware pre and post-processing for JPEG images using deep learning architectures

We introduce a learning-based CNN architecture for both pre- and post-processing in conjunction with the standard JPEG codec to enhance compression artifact removal. Our approach advances previous methods that use compression-decompression networks by incorporating: (a) an edge-aware loss function designed to reduce blurring, a common issue in earlier works, alongside a super-resolution CNN for post-processing, which improves rate-distortion performance at low bit rates; (b) distinct implementations for compact and full-resolution image representations, tailored for low and high bit rates, respectively. Compared to JPEG, JPEG2000, BPG, VVC-Intra, and recent CNN-based methods, our algorithm demonstrates significant improvements in PSNR, with gains of up to 21% and 25% at low (0.2-0.3 bpp) and high bit rates (0.5-0.7 bpp), respectively. Additionally, the MSSSIM gain reaches approximately 71% and 65% at low and high bit rates, respectively. This timely method has substantial potential to engage multimedia industries, researchers, and standard-setting agencies.