  In this paper, we propose a temporal group alignment and fusion network to
enhance the quality of compressed videos by using the long-short term
correlations between frames. The proposed model consists of the intra-group
feature alignment (IntraGFA) module, the inter-group feature fusion (InterGFF)
module, and the feature enhancement (FE) module. We form the group of pictures
(GoP) by selecting frames from the video according to their temporal distances
to the target enhanced frame. With this grouping, the composed GoP can contain
either long- or short-term correlated information of neighboring frames. We
design the IntraGFA module to align the features of frames of each GoP to
eliminate the motion existing between frames. We construct the InterGFF module
to fuse features belonging to different GoPs and finally enhance the fused
features with the FE module to generate high-quality video frames. The
experimental results show that our proposed method achieves up to 0.05dB gain
and lower complexity compared to the state-of-the-art method.
