Enhanced Video Super-Resolution Network towards Compressed Data

Feng LI, Yixuan WU, Anqi LI, Huihui BAI*, Runmin CONG, Yao ZHAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


Video super-resolution (VSR) algorithms aim at recovering a temporally consistent high-resolution (HR) video from its corresponding low-resolution (LR) video sequence. Due to the limited bandwidth during video transmission, most available videos on the internet are compressed. Nevertheless, few existing algorithms consider the compression factor in practical applications. In this paper, we propose an enhanced VSR model towards compressed videos, termed as ECVSR, to simultaneously achieve compression artifacts reduction and SR reconstruction end-to-end. ECVSR contains a motion-excited temporal adaption network (METAN) and a multi-frame SR network (SRNet). The METAN takes decoded LR video frames as input and models inter-frame correlations via bidirectional deformable alignment and motion-excited temporal adaption, where temporal differences are calculated as motion prior to excite the motion-sensitive regions of temporal features. In SRNet, cascaded recurrent multi-scale blocks (RMSB) are employed to learn deep spatio-temporal representations from adapted multi-frame features. Then, we build a reconstruction module for spatio-temporal information integration and HR frame reconstruction, which is followed by a detail refinement module for texture and visual quality enhancement. Extensive experimental results on compressed videos demonstrate the superiority of our method for compressed VSR.

Original languageEnglish
Article number202
Number of pages21
JournalACM Transactions on Multimedia Computing, Communications and Applications
Issue number7
Early online date25 Apr 2024
Publication statusPublished - Jul 2024
Externally publishedYes

Bibliographical note

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© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.


  • Compressed video super-resolution
  • motion-excited temporal adaption
  • multi-frame SR network
  • video quality enhancement


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