Restoration of User Videos Shared on Social Media
Hongming Luo, Fei Zhou, Kin-man Lam, and Guoping Qiu

TL;DR
This paper introduces VOTES, a novel video restoration framework that explicitly models degradation features to improve the quality of user videos shared on social media, outperforming existing methods.
Contribution
The paper proposes a new degradation feature map (DFM) concept and an adaptive estimation process to guide video restoration, along with a large real-world user video dataset.
Findings
VOTES outperforms state-of-the-art methods quantitatively.
VOTES achieves superior qualitative restoration results.
A large-scale real-world user video database is provided.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Image and Video Quality Assessment · Advanced Image Processing Techniques
