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Video Super Resolution On Msu Super 1

评估指标

BSQ-rate over ERQA
BSQ-rate over LPIPS
BSQ-rate over MS-SSIM
BSQ-rate over PSNR
BSQ-rate over VMAF

评测结果

各个模型在此基准测试上的表现结果

Paper TitleRepository
bicubic + uavs3e21.96518.0575.0687.3727.026--
RealSR + vvenc21.96518.34411.64315.14410.67Real-World Super-Resolution via Kernel Estimation and Noise Injection-
bicubic + aomenc21.96517.4582.8888.3956.797--
bicubic + vvenc21.96516.8945.73512.7878.635--
TMNet + aomenc21.7986.27610.32215.1444.667Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
TMNet + vvenc21.30313.9881.8139.431.795Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
RSDN + aomenc20.61714.57411.64315.14410.67Video Super-Resolution with Recurrent Structure-Detail Network
SOF-VSR-BI + vvenc18.84411.2734.8829.2454.527Deep Video Super-Resolution using HR Optical Flow Estimation
SOF-VSR-BI + x26518.54511.2364.5589.073.565Deep Video Super-Resolution using HR Optical Flow Estimation
LGFN + vvenc18.34211.7590.8895.7681.626Local-Global Fusion Network for Video Super-Resolution-
VRT + vvenc18.33311.4960.8365.7770.652VRT: A Video Restoration Transformer
BasicVSR + vvenc18.33311.5610.9195.7810.676BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
RSDN + uavs3e18.32713.84411.64315.1449.796Video Super-Resolution with Recurrent Structure-Detail Network
RBPN + vvenc18.31411.7770.8845.7830.689Recurrent Back-Projection Network for Video Super-Resolution
EGVSR + aomenc16.7335.6711.64315.14410.67Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
bicubic + x26516.01415.3634.5636.4736.469--
DBVSR + vvenc15.98811.4350.8985.7650.698Deep Blind Video Super-resolution
SOF-VSR-BD + vvenc15.95813.4942.1128.0276.41Deep Video Super-Resolution using HR Optical Flow Estimation
SOF-VSR-BD + aomenc15.114.0347.54613.0767.464Deep Video Super-Resolution using HR Optical Flow Estimation
RSDN + vvenc14.954.8669.13814.06110.145Video Super-Resolution with Recurrent Structure-Detail Network
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