  The block-based coding structure in the hybrid video coding framework
inevitably introduces compression artifacts such as blocking, ringing, etc. To
compensate for those artifacts, extensive filtering techniques were proposed in
the loop of video codecs, which are capable of boosting the subjective and
objective qualities of reconstructed videos. Recently, neural network based
filters were presented with the power of deep learning from a large magnitude
of data. Though the coding efficiency has been improved from traditional
methods in High-Efficiency Video Coding (HEVC), the rich features and
information generated by the compression pipeline has not been fully utilized
in the design of neural networks. Therefore, in this paper, we propose the
Residual-Reconstruction-based Convolutional Neural Network (RRNet) to further
improve the coding efficiency to its full extent, where the compression
features induced from bitstream in form of prediction residual is fed into the
network as an additional input to the reconstructed frame. In essence, the
residual signal can provide valuable information about block partitions and can
aid reconstruction of edge and texture regions in a picture. Thus, more
adaptive parameters can be trained to handle different texture characteristics.
The experimental results show that our proposed RRNet approach presents
significant BD-rate savings compared to HEVC and the state-of-the-art CNN-based
schemes, indicating that residual signal plays a significant role in enhancing
video frame reconstruction.
