SOTAVerified

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.

Papers

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Showing 1–10 of 216 papers

TitleStatusHype
Bridging Video Quality Scoring and Justification via Large Multimodal Models—0
EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment—0
TDVE-Assessor: Benchmarking and Evaluating the Quality of Text-Driven Video Editing with LMMs—0
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and ResultsCode0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—0
Semantically-Aware Game Image Quality Assessment—0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor—0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study—0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and ResultsCode1
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All datasetsMSU SR-QA DatasetKoNViD-1kMSU NR VQA DatabaseLIVE-VQCMSU FR VQA DatabaseYouTube-UGCLIVE-FB LSVQLIVE-ETRILIVE LivestreamLIVE-YT-HFR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-GREEDSRCC0.88—Unverified
2GREED-VMAFSRCC0.87—Unverified
3GSTISRCC0.81—Unverified