Restoration of User Videos Shared on Social Media

Hongming Luo, Fei Zhou, Kin Man Lam, Guoping Qiu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


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

Original languageEnglish
Title of host publicationMM 2022 - Proceedings of the 30th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450392037
Publication statusPublished - 10 Oct 2022
Event30th ACM International Conference on Multimedia, MM 2022 - Lisboa, Portugal
Duration: 10 Oct 202214 Oct 2022

Publication series

NameMM 2022 - Proceedings of the 30th ACM International Conference on Multimedia


Conference30th ACM International Conference on Multimedia, MM 2022


  • degradation sensing
  • social media
  • video restoration

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software


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