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 101–150 of 216 papers

TitleStatusHype
DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor—0
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment—0
Enhancing VMAF through New Feature Integration and Model Combination—0
ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos—0
EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment—0
FineVQ: Fine-Grained User Generated Content Video Quality Assessment—0
FOVQA: Blind Foveated Video Quality Assessment—0
FSIM: A Feature Similarity Index for Image Quality Assessment—0
Full-reference Video Quality Assessment for User Generated Content Transcoding—0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human—0
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos—0
HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content—0
High Frame Rate Video Quality Assessment using VMAF and Entropic Differences—0
Highly Efficient No-reference 4K Video Quality Assessment with Full-Pixel Covering Sampling and Training Strategy—0
Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric—0
HVS Revisited: A Comprehensive Video Quality Assessment Framework—0
Image information and visual quality—0
Image quality assessment: from error visibility to structural similarity—0
Image quality prediction using synthetic and natural codebooks: comparative results—0
Improving precision of objective image/video quality metrics—0
InternVQA: Advancing Compressed Video Quality Assessment with Distilling Large Foundation Model—0
Large-Scale Study of Perceptual Video Quality—0
Latent Factor Modeling of Users Subjective Perception for Stereoscopic 3D Video Recommendation—0
Latent Image and Video Resolution Prediction using Convolutional Neural Networks—0
Learning Human Cognitive Appraisal Through Reinforcement Memory Unit—0
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment—0
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions—0
Making Video Quality Assessment Models Robust to Bit Depth—0
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation—0
MRET: Multi-resolution Transformer for Video Quality Assessment—0
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment—0
MVAD: A Multiple Visual Artifact Detector for Video Streaming—0
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics—0
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild—0
NTIRE 2023 Quality Assessment of Video Enhancement Challenge—0
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge—0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study—0
One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment—0
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization—0
PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition—0
PEA265: Perceptual Assessment of Video Compression Artifacts—0
Perceptual Quality Assessment of UGC Gaming Videos—0
Perceptual Video Quality Assessment: A Survey—0
Prediction of the Influence of Navigation Scan-path on Perceived Quality of Free-Viewpoint Videos—0
Priorformer: A UGC-VQA Method with content and distortion priors—0
PRNet: A Progressive Regression Network for No-Reference User-Generated-Content Video Quality Assessment—0
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild—0
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models—0
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
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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
1PieAPPSROCC0.75—Unverified
2Q-Align (IQA)SROCC0.75—Unverified
3Q-Align (VQA)SROCC0.72—Unverified
4PaQ-2-PiQSROCC0.71—Unverified
5DBCNNSROCC0.69—Unverified
6MUSIQ trained on PaQ-2-PiQSROCC0.68—Unverified
7Ma-MetricSROCC0.67—Unverified
8MANIQASROCC0.67—Unverified
9ClipIQA+ ResNet50SROCC0.66—Unverified
10MUSIQ trained on SPAQSROCC0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1DOVER (end-to-end)PLCC0.91—Unverified
2FasterVQA (fine-tuned)PLCC0.9—Unverified
3DOVER (head-only)PLCC0.89—Unverified
4FAST-VQA (finetuned on KonViD-1k)PLCC0.89—Unverified
5ReLaX-VQA (finetuned on KoNViD-1k)PLCC0.87—Unverified
6SimpleVQAPLCC0.86—Unverified
7DisCoVQAPLCC0.86—Unverified
8HVS-5MPLCC0.86—Unverified
9FAST-VQA (trained on LSVQ only)PLCC0.86—Unverified
10CONVIQTPLCC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MDTVSFASRCC0.93—Unverified
2DBCNNSRCC0.92—Unverified
3UNIQUESRCC0.91—Unverified
4LISRCC0.91—Unverified
5LINEARITYSRCC0.91—Unverified
6VSFASRCC0.9—Unverified
7MUSIQSRCC0.9—Unverified
8DOVERSRCC0.89—Unverified
9SPAQ MT-SSRCC0.88—Unverified
10SPAQ BLSRCC0.88—Unverified
#ModelMetricClaimedVerifiedStatus
1ReLaX-VQA (finetuned on LIVE-VQC)PLCC0.89—Unverified
2DOVER (end-to-end)PLCC0.87—Unverified
3DOVER (head-only)PLCC0.86—Unverified
4FAST-VQA (finetuned on LIVE-VQC)PLCC0.86—Unverified
5FasterVQA (fine-tuned)PLCC0.86—Unverified
6FAST-VQA (trained on LSVQ only)PLCC0.84—Unverified
7DisCoVQAPLCC0.84—Unverified
8HVS-5MPLCC0.84—Unverified
9BVQA-2022PLCC0.84—Unverified
102BiVQAPLCC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1VMAF Y (v061)SRCC0.94—Unverified
2VMAF Y (v062)SRCC0.94—Unverified
3VMAF Y (v063)SRCC0.94—Unverified
4AHIQSRCC0.94—Unverified
5VMAF Y (v061_neg)SRCC0.91—Unverified
6VIFpSRCC0.91—Unverified
7VSISRCC0.91—Unverified
8MS-SSIMSRCC0.9—Unverified
9SR-SIMSRCC0.9—Unverified
10FSIMSRCC0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1DOVER (end-to-end)PLCC0.87—Unverified
2ReLaX-VQA (finetuned on YouTube-UGC)PLCC0.87—Unverified
3DOVER (head-only)PLCC0.86—Unverified
4FasterVQA (fine-tuned)PLCC0.86—Unverified
5SimpleVQAPLCC0.86—Unverified
6FAST-VQA (finetuned on YouTube-UGC)PLCC0.85—Unverified
7HVS-5MPLCC0.85—Unverified
8ReLaX-VQA (trained on LSVQ only)PLCC0.84—Unverified
9CONVIQTPLCC0.82—Unverified
10ReLaX-VQAPLCC0.82—Unverified
#ModelMetricClaimedVerifiedStatus
1OneAlign + FAST-VQAPLCC0.9—Unverified
2DOVERPLCC0.89—Unverified
3OneAlignPLCC0.89—Unverified
4FAST-VQAPLCC0.88—Unverified
5FasterVQAPLCC0.87—Unverified
6HVS-5MPLCC0.87—Unverified
7SimpleVQAPLCC0.86—Unverified
8BVQA-2022PLCC0.85—Unverified
9DisCoVQAPLCC0.85—Unverified
10PVQPLCC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1CONVIQTSRCC0.94—Unverified
2CONTRIQUESRCC0.93—Unverified
3ChipQASRCC0.63—Unverified
4VBLIINDSSRCC0.48—Unverified
5ChipQA-0SRCC0.4—Unverified
6BRISQUESRCC0.27—Unverified
7TLVQMSRCC0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1ChipQASRCC0.76—Unverified
2ChipQA-0SRCC0.75—Unverified
3TLVQMSRCC0.75—Unverified
4RAPIQUESRCC0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-GREEDSRCC0.88—Unverified
2GREED-VMAFSRCC0.87—Unverified
3GSTISRCC0.81—Unverified