A Novel Deep Learning-Based Method of Improving Coding Efficiency from the Decoder-End for HEVC

Improving the coding efficiency is the eternal theme in video coding field. The traditional way for this purpose is to reduce the redundancies inside videos by adding numerous coding options at the encoder side. However, no matter what we have done, it is still hard to guarantee the optimal coding efficiency. On the other hand, the decoded video can be treated as a certain compressive sampling of the original video. According to the compressive sensing theory, it might be possible to further enhance the quality of the decoded video by some restoration methods. Different from the traditional methods, without changing the encoding algorithm, this paper focuses on an approach to improve the video's quality at the decoder end, which equals to further boosting the coding efficiency. Furthermore, we propose a very deep convolutional neural network to automatically remove the artifacts and enhance the details of HEVC-compressed videos, by utilizing that underused information left in the bit-streams and external images. Benefit from the prowess and efficiency of the fully end-to-end feed forward architecture, our approach can be treated as a better decoder to efficiently obtain the decoded frames with higher quality. Extensive experiments indicate our approach can further improve the coding efficiency post the deblocking and SAO in current HEVC decoder, averagely 5.0%, 6.4%, 5.3%, 5.5% BD-rate reduction for all intra, lowdelay P, lowdelay B and random access configurations respectively. This method can aslo be extended to any video coding standards.