  Integrating deep learning techniques into the video coding framework gains
significant improvement compared to the standard compression techniques,
especially applying super-resolution (up-sampling) to down-sampling based video
coding as post-processing. However, besides up-sampling degradation, the
various artifacts brought from compression make super-resolution problem more
difficult to solve. The straightforward solution is to integrate the artifact
removal techniques before super-resolution. However, some helpful features may
be removed together, degrading the super-resolution performance. To address
this problem, we proposed an end-to-end restoration-reconstruction deep neural
network (RR-DnCNN) using the degradation-aware technique, which entirely solves
degradation from compression and sub-sampling. Besides, we proved that the
compression degradation produced by Random Access configuration is rich enough
to cover other degradation types, such as Low Delay P and All Intra, for
training. Since the straightforward network RR-DnCNN with many layers as a
chain has poor learning capability suffering from the gradient vanishing
problem, we redesign the network architecture to let reconstruction leverages
the captured features from restoration using up-sampling skip connections. Our
novel architecture is called restoration-reconstruction u-shaped deep neural
network (RR-DnCNN v2.0). As a result, our RR-DnCNN v2.0 outperforms the
previous works and can attain 17.02% BD-rate reduction on UHD resolution for
all-intra anchored by the standard H.265/HEVC. The source code is available at
https://minhmanho.github.io/rrdncnn/.
