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.

翻译：近年来，深度学习方法在压缩视频质量增强任务中展现出显著优势。现有方法通常以原始视频为真实值，从包含多种伪影的连续帧中提取实用信息。然而，这些方法未能充分利用压缩视频和原始视频中的有效信息来引导压缩视频的质量增强。本文提出了一种独特有效的信息引导方案（VIG），通过挖掘压缩视频和原始视频中的有效信息来增强压缩视频质量。具体而言，我们提出了一个高效框架——压缩冗余过滤网络（CRF），以平衡速度与增强效果。通过信息过滤去除冗余后，CRF可利用压缩视频的有效信息重建纹理。此外，我们提出了一种渐进式真实引导蒸馏策略（TGD），该策略无需设计额外的教师模型和蒸馏损失函数。仅通过使用真实值作为输入来引导模型聚合原始帧间正确的时空对应关系，TGD即可在不增加额外训练成本的情况下显著提升增强效果。大量实验表明，我们的方法在压缩视频质量增强的准确性和效率方面均达到了最先进水平。