Exploring Spatiotemporal Relationships for Improving Compressed Video Quality

The compressed video quality enhancement problem aims at restoring the distorted information of the compressed video. Details in high-quality reference frames are usually used to improve the detailed information and reduce the artifacts of the low-quality target frames. In this paper, we propose a novel network to extract helpful information from peak quality frames and then incorporate it into low-quality frames. The proposed network first integrates the information from both the target and reference frames, and then fully spatiotemporal deformable convolutions are used to better adapt the sampling strategy of the input frames. In addition, the temporal offset calibration procedure is also applied to reduce the loss of high-frequency components in the interpolation process. The results on benchmark datasets show that our method achieves competitive performance compared with other existing methods.