Methods › Computer Vision › Video Model Blocks › WRQE
Weighted Recurrent Quality Enhancement
WRQE
Introduced by Ren Yang et al. in Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Weighted Recurrent Quality Enhancement, or WRQE, is a recurrent quality enhancement network for video compression that takes both compressed frames and the bit stream as inputs. In the recurrent cell of WRQE, the memory and update signal are weighted by quality features to reasonably leverage multi-frame information for enhancement.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement 4 Mar 2020 · 3 repositories · arXiv:2003.01966Syntology ran 0 of 2 samples · 2 unverified
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Decoder | 1 |
| Image Compression | 1 |
| MS-SSIM | 1 |
| SSIM | 1 |
| Video Compression | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections