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SotA/Computer Vision/Video Super-Resolution

Video Super-Resolution

43 benchmarks281 papers

Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.

<span style="color:grey; opacity: 0.6">( Image credit: Detail-revealing Deep Video Super-Resolution )</span>

Benchmarks

Video Super-Resolution on MSU Super-Resolution for Video Compression

BSQ-rate over ERQABSQ-rate over PSNRBSQ-rate over MS-SSIMBSQ-rate over LPIPSBSQ-rate over VMAFBSQ-rate over Subjective Score

Video Super-Resolution on MSU Video Upscalers: Quality Enhancement

SSIMPSNRLPIPSVMAF

Video Super-Resolution on MSU Video Super Resolution Benchmark: Detail Restoration

Subjective scoreERQAv1.01 - LPIPSSSIMQRCRv1.0PSNRFPS

Video Super-Resolution on Vid4 - 4x upscaling

PSNRSSIMMOVIE

Video Super-Resolution on Vid4 - 4x upscaling - BD degradation

PSNRSSIM

Video Super-Resolution on UDM10 - 4x upscaling

PSNRSSIM

Video Super-Resolution on REDS4- 4x upscaling

PSNRSSIM

Video Super-Resolution on Ultra Video Group HD - 4x upscaling

Average PSNR

Video Super-Resolution on Falling Objects

SSIMPSNRTIoU

Video Super-Resolution on TbD

SSIMPSNRTIoU

Video Super-Resolution on TbD-3D

SSIMPSNRTIoU

Video Super-Resolution on Vimeo90K

PSNRSSIM

Video Super-Resolution on Xiph HD - 4x upscaling

Average PSNR

Video Super-Resolution on SAT-MTB-VSR

PSNR

Video Super-Resolution on SPMCS - 4x upscaling

PSNRSSIM

Video Super-Resolution on Vimeo-90K

Average PSNR