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.

翻译：近年来，深度学习技术的进步显著提升了压缩视频的质量。然而，现有方法并未充分利用压缩视频的运动特性，例如视频内容间的剧烈运动变化以及压缩视频的分层编码结构。本研究提出一种新颖框架，通过利用视频压缩的低延迟配置来增强当前最先进方法BasicVSR++的性能。我们引入一种上下文自适应视频融合方法，以提升压缩视频的最终质量。所提出的方法已在视频修复与增强基准测试NTIRE22挑战赛中进行了评估，与先前方法相比，在定量指标和视觉质量上均取得了提升。