# Leveraging Video Coding Knowledge for Deep Video Enhancement

## TL;DR

This paper introduces a novel deep learning framework that leverages video coding knowledge, specifically the hierarchical structure and motion characteristics, to significantly improve compressed video quality.

## Contribution

It proposes a new method that incorporates video coding insights into deep video enhancement, outperforming existing techniques in benchmarks.

## Key findings

- Achieved higher quantitative metrics in NTIRE22 challenge.
- Improved visual quality of compressed videos.
- Enhanced state-of-the-art method with coding knowledge integration.

## Abstract

Recent advancements in deep learning techniques have significantly improved the quality of compressed videos. However, previous approaches have not fully exploited the motion characteristics of compressed videos, such as the drastic change in motion between video contents and the hierarchical coding structure of the compressed video. This study proposes a novel framework that leverages the low-delay configuration of video compression to enhance the existing state-of-the-art method, BasicVSR++. We incorporate a context-adaptive video fusion method to enhance the final quality of compressed videos. The proposed approach has been evaluated in the NTIRE22 challenge, a benchmark for video restoration and enhancement, and achieved improvements in both quantitative metrics and visual quality compared to the previous method.