SRCC: 0.8308PLCC: 0.8613KLCC: 0.6498Type: NR
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Video Quality Assessment
Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ. SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
논문
SRCC: 0.8871PLCC: 0.9099KLCC: 0.7216Type: NR
SRCC: 0.8822PLCC: 0.8814KLCC: 0.7186Type: NR
SRCC: 0.8742PLCC: 0.8933KLCC: 0.7037Type: NR
SRCC: 0.5985PLCC: 0.6713KLCC: 0.4215Type: NR
SRCC: 0.9220PLCC: 0.9222KLCC: 0.7750Type: NR
SRCC: 0.9131PLCC: 0.9270KLCC: 0.7640Type: NR
SRCC: 0.8064
SRCC: 0.8705PLCC: 0.8549KLCC: 0.7079Type: NR
SRCC: 0.8658
SRCC: 0.8360PLCC: 0.8464KLCC: 0.6608Type: NR
SRCC: 0.9004PLCC: 0.9068KLCC: 0.7433Type: NR
PLCC: 0.858
SRCC: 0.8494PLCC: 0.8784KLCC: 0.6745Type: NR
SRCC: 0.9104PLCC: 0.9106KLCC: 0.7589Type: NR
SRCC: 0.9049PLCC: 0.9180KLCC: 0.7483Type: NR
PLCC: 0.8876
SRCC: 0.8822
SRCC: 0.7286PLCC: 0.7717KLCC: 0.5414Type: NR
SRCC: 0.9289PLCC: 0.9431KLCC: 0.7883Type: NR
SRCC: 0.9148PLCC: 0.9238KLCC: 0.7648Type: NR