  In recent years deep learning methods have shown great superiority in
compressed video quality enhancement tasks. Existing methods generally take the
raw video as the ground truth and extract practical information from
consecutive frames containing various artifacts. However, they do not fully
exploit the valid information of compressed and raw videos to guide the quality
enhancement for compressed videos. In this paper, we propose a unique Valid
Information Guidance scheme (VIG) to enhance the quality of compressed videos
by mining valid information from both compressed videos and raw videos.
Specifically, we propose an efficient framework, Compressed Redundancy
Filtering (CRF) network, to balance speed and enhancement. After removing the
redundancy by filtering the information, CRF can use the valid information of
the compressed video to reconstruct the texture. Furthermore, we propose a
progressive Truth Guidance Distillation (TGD) strategy, which does not need to
design additional teacher models and distillation loss functions. By only using
the ground truth as input to guide the model to aggregate the correct
spatio-temporal correspondence across the raw frames, TGD can significantly
improve the enhancement effect without increasing the extra training cost.
Extensive experiments show that our method achieves the state-of-the-art
performance of compressed video quality enhancement in terms of accuracy and
efficiency.
