User videos shared on social media platforms usually suffer from degradations
caused by unknown proprietary processing procedures, which means that their
visual quality is poorer than that of the originals. This paper presents a new
general video restoration framework for the restoration of user videos shared
on social media platforms. In contrast to most deep learning-based video
restoration methods that perform end-to-end mapping, where feature extraction
is mostly treated as a black box, in the sense that what role a feature plays
is often unknown, our new method, termed Video restOration through adapTive
dEgradation Sensing (VOTES), introduces the concept of a degradation feature
map (DFM) to explicitly guide the video restoration process. Specifically, for
each video frame, we first adaptively estimate its DFM to extract features
representing the difficulty of restoring its different regions. We then feed
the DFM to a convolutional neural network (CNN) to compute hierarchical
degradation features to modulate an end-to-end video restoration backbone
network, such that more attention is paid explicitly to potentially more
difficult to restore areas, which in turn leads to enhanced restoration
performance. We will explain the design rationale of the VOTES framework and
present extensive experimental results to show that the new VOTES method
outperforms various state-of-the-art techniques both quantitatively and
qualitatively. In addition, we contribute a large scale real-world database of
user videos shared on different social media platforms. Codes and datasets are
available at this https URL

本文提出了一种新的视频修复框架 VOTES，它通过引入退化特征映射来显式地指导视频修复过程，利用深度学习技术实现对社交媒体上用户视频的修复，数据源包含了大规模的真实世界数据库。