Two-stage Multi-frame Cooperative Quality Enhancement on Compressed Video
Shengjie Chen, Mao Ye · 2021
With the great success of deep learning network, compressed video quality enhancement methods based on deep learning are mushrooming. Most of these methods ignore the correlation between frames and do not make full use of the information of adjacent frames. We propose a two-stage multi-frame cooperative quality enhancement network. Our method consist of two main modules: motion compensation network and quality enhancement network. We use a two-stage enhanced structure to make full use of high-quality frames information and realize the multi-frame cooperative enhancement of a Group of Pictures(GOP), fully considering the correlation between frames. The experimental results on the HEVC standard test sequences show that the proposed method is improved by about 10% compared with MFQE2.0.