Video Quality Assessment on LIVE-YT-Gaming
0.882SRCCDOVER
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| DOVER2024.02 | 0.882 | 0.906 | — | — | |
| FastVQA2024.02 | 0.869 | 0.88 | — | — | |
| qstRectifiers=Spatial and Temporal2024.02 | 0.867 | 0.902 | — | — | |
| SimpleVQAType=VQA2022.04 | 0.861 | 0.866 | — | — | |
| CONVIQT2026.02 | 0.8609 | 0.885 | 0.6806 | 7.8534 | |
| qbRectifiers=None2024.02 | 0.859 | 0.895 | — | — | |
| qsRectifiers=Spatial2024.02 | 0.857 | 0.898 | — | — | |
| qtRectifiers=Temporal2024.02 | 0.857 | 0.894 | — | — | |
| GAME-VQP2026.02 | 0.8563 | 0.8754 | — | 8.533 | |
| CONTRIQUE2026.02 | 0.8556 | 0.8791 | 0.6738 | 8.0265 | |
| Li222024.02 | 0.852 | 0.868 | — | — | |
| MTL-VQA2026.02 | 0.8486 | 0.8758 | 0.6681 | 8.133 | |
| SimpleVQA2024.02 | 0.814 | 0.836 | — | — | |
| GAMIVAL2026.02 | 0.8111 | 0.8321 | — | 9.2995 | |
| VIDEVAL2026.02 | 0.8071 | 0.9119 | — | 10.093 | |
| VIDEVALType=VQA2022.04 | 0.807 | 0.812 | — | — | |
| RAPIQUEType=VQA2022.04 | 0.803 | 0.825 | — | — | |
| RAPIQUE2026.02 | 0.8028 | 0.8248 | — | 9.661 | |
| VSFA2024.02 | 0.784 | 0.819 | — | — | |
| VSFA2026.02 | 0.7762 | 0.8014 | — | 10.396 | |
| VSFAType=VQA2022.04 | 0.776 | 0.801 | — | — | |
| VersusQMethod Category=Reasoning or reinforcement-learning based LMM evaluators2026.05 | 0.774 | 0.814 | — | — | |
| VQAThinkerMethod Category=Reasoning or reinforcement-learning based LMM evaluators2026.05 | 0.767 | 0.806 | — | — | |
| TLVQM2026.02 | 0.7484 | 0.7564 | — | 11.134 | |
| TLVQMType=VQA2022.04 | 0.748 | 0.756 | — | — | |
| ResNet50Type=IQA2022.04 | 0.729 | 0.768 | — | — | |
| LMM-PVQA (Soft Ranking Stage 2)Training data=PLVD-P1/ P2 (600k), Human Label=No, Setting=Zero-shot2025.05 | 0.717 | 0.763 | — | — | |
| LMM-PVQA (Hard Ranking)Training data=PLVD-P1 (500k), Human Label=No, Setting=Zero-shot2025.05 | 0.705 | 0.742 | — | — | |
| LMM-PVQA (Soft Ranking Stage 3)Training data=PLVD-P1/ P2/ P3 (700k), Human Label=No, Setting=Zero-shot2025.05 | 0.703 | 0.761 | — | — | |
| LMM-PVQA (Soft Ranking Stage 1)Training data=PLVD-P1 (500k), Human Label=No, Setting=Zero-shot2025.05 | 0.697 | 0.752 | — | — | |
| MinimalisticVQA(IX)Training data=LSVQ [70], Human Label=Yes, Setting=Zero-shot2025.05 | 0.686 | 0.746 | — | — | |
| MinimalisticVQAMethod Category=Supervised VQA/IQA models2026.05 | 0.686 | 0.746 | — | — | |
| SimpleVQAMethod Category=Supervised VQA/IQA models2026.05 | 0.657 | 0.728 | — | — | |
| DOVER++2026.02 | 0.653 | 0.7258 | 0.4765 | 11.6374 | |
| DOVERTraining data=LSVQ [70], Human Label=Yes, Setting=Zero-shot2025.05 | 0.647 | 0.728 | — | — | |
| DOVERMethod Category=Supervised VQA/IQA models2026.05 | 0.647 | 0.728 | — | — | |
| KonCept512Type=IQA2022.04 | 0.643 | 0.649 | — | — | |
| VGG19Type=IQA2022.04 | 0.638 | 0.658 | — | — | |
| FAST-VQATraining data=LSVQ [70], Human Label=Yes, Setting=Zero-shot2025.05 | 0.631 | 0.677 | — | — | |
| FAST-VQAMethod Category=Supervised VQA/IQA models2026.05 | 0.631 | 0.677 | — | — | |
| VQA2-ScorerMethod Category=Supervised VQA/IQA models2026.05 | 0.613 | 0.698 | — | — | |
| Q-AlignTraining data=fused [11, 17, 28, 40, 70], Human Label=Yes, Setting=Zero-shot2025.05 | 0.611 | 0.681 | — | — | |
| Q-AlignMethod Category=Supervised VQA/IQA models2026.05 | 0.611 | 0.681 | — | — | |
| BRISQUEType=IQA2022.04 | 0.604 | 0.513 | — | — | |
| BRISQUE2026.02 | 0.6037 | 0.6383 | — | 16.208 | |
| MinimalisticVQA(VII)Training data=LSVQ [70], Human Label=Yes, Setting=Zero-shot2025.05 | 0.596 | 0.682 | — | — | |
| VisualQuality-R1Method Category=Reasoning or reinforcement-learning based LMM evaluators2026.05 | 0.472 | 0.548 | — | — | |
| NDNet-Gaming2026.02 | 0.4562 | 0.469 | — | 14.941 | |
| CLIP-IQA+2026.02 | 0.4315 | 0.5372 | 0.2951 | 14.2693 | |
| ReIQA2026.02 | 0.373 | 0.3912 | 0.2609 | 15.5303 | |
| CLIP-IQAMethod Category=Training-free / unsupervised quality models2026.05 | 0.358 | 0.384 | — | — | |
| V-BLIINDSType=VQA2022.04 | 0.357 | 0.403 | — | — | |
| GM-LOGType=IQA2022.04 | 0.312 | 0.317 | — | — | |
| Q-InsightMethod Category=Reasoning or reinforcement-learning based LMM evaluators2026.05 | 0.31 | 0.326 | — | — | |
| NIQE2026.02 | 0.2801 | 0.3037 | — | 16.208 | |
| NIQEType=IQA2022.04 | 0.28 | 0.304 | — | — | |
| FasterVQA2026.02 | 0.2603 | 0.2889 | 0.1767 | 16.1962 | |
| NIQETraining data=None, Human Label=No, Setting=Zero-shot2025.05 | 0.24 | 0.247 | — | — | |
| NIQEMethod Category=Training-free / unsupervised quality models2026.05 | 0.24 | 0.247 | — | — | |
| IL-NIQETraining data=None, Human Label=No, Setting=Zero-shot2025.05 | 0.2 | 0.168 | — | — | |
| STEMTraining data=None, Human Label=No, Setting=Zero-shot2025.05 | 0.103 | 0.111 | — | — | |
| STEMMethod Category=Training-free / unsupervised quality models2026.05 | 0.103 | 0.111 | — | — | |
| VIIDEOTraining data=None, Human Label=No, Setting=Zero-shot2025.05 | 0.077 | -0.199 | — | — | |
| VIIDEOMethod Category=Training-free / unsupervised quality models2026.05 | 0.077 | -0.199 | — | — |