# Valid Information Guidance Network for Compressed Video Quality Enhancement

## TL;DR

This paper introduces a novel framework that leverages valid information from both compressed and raw videos to enhance compressed video quality efficiently, achieving state-of-the-art results without extra training costs.

## Contribution

The paper proposes a unique Valid Information Guidance scheme and a CRF network with a TGD strategy that improves quality enhancement by effectively utilizing information from raw and compressed videos.

## Key findings

- Achieves state-of-the-art performance in video quality enhancement.
- Balances speed and enhancement effectiveness.
- Does not require additional teacher models for distillation.

## Abstract

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.