  We propose a learning-based compression scheme that envelopes a standard
codec between pre and post-processing deep CNNs. Specifically, we demonstrate
improvements over prior approaches utilizing a compression-decompression
network by introducing: (a) an edge-aware loss function to prevent blurring
that is commonly occurred in prior works & (b) a super-resolution convolutional
neural network (CNN) for post-processing along with a corresponding
pre-processing network for improved rate-distortion performance in the low rate
regime. The algorithm is assessed on a variety of datasets varying from low to
high resolution namely Set 5, Set 7, Classic 5, Set 14, Live 1, Kodak, General
100, CLIC 2019. When compared to JPEG, JPEG2000, BPG, and recent CNN approach,
the proposed algorithm contributes significant improvement in PSNR with an
approximate gain of 20.75%, 8.47%, 3.22%, 3.23% and 24.59%, 14.46%, 10.14%,
8.57% at low and high bit-rates respectively. Similarly, this improvement in
MS-SSIM is approximately 71.43%, 50%, 36.36%, 23.08%, 64.70% and 64.47%,
61.29%, 47.06%, 51.52%, 16.28% at low and high bit-rates respectively. With
CLIC 2019 dataset, PSNR is found to be superior with approximately 16.67%,
10.53%, 6.78%, and 24.62%, 17.39%, 14.08% at low and high bit-rates
respectively, over JPEG2000, BPG, and recent CNN approach. Similarly, the
MS-SSIM is found to be superior with approximately 72%, 45.45%, 39.13%, 18.52%,
and 71.43%, 50%, 41.18%, 17.07% at low and high bit-rates respectively,
compared to the same approaches. A similar type of improvement is achieved with
other datasets also.
