computer-vision-20
Video Quality Assessment
3260 papers • 126 benchmarks • 313 datasets
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.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in video-quality-assessment-60
Trend
Dataset
Best Model
Actions
MSU SR-QA Dataset
MSU SR-QA Dataset
MSU NR VQA Database
MSU NR VQA Database
KoNViD-1k
Libraries
iUse these libraries to find video-quality-assessment-60 models and implementations
3 papers 217
Datasets
No datasets available.
Subtasks
No subtasks available.
