Blind Image Quality Assessment on LIVE
0.984SRCCTOPIQ
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| TOPIQ2026.04 | 0.984 | 0.984 | |
| GLIANetNumber of trainable parameters=8M2026.05 | 0.983 | 0.984 | |
| VUGA2026.04 | 0.982 | 0.985 | |
| SHDIQANumber of trainable parameters=24M2026.05 | 0.982 | 0.984 | |
| DEIQT2026.04 | 0.98 | 0.982 | |
| DEFNet2025.07 | 0.978 | 0.96 | |
| CDINet2025.07 | 0.977 | 0.975 | |
| LoDa2026.04 | 0.975 | 0.979 | |
| LoDaNumber of trainable parameters=9M2026.05 | 0.975 | 0.979 | |
| VCRNet2025.07 | 0.973 | 0.974 | |
| DPNet2025.07 | 0.971 | 0.971 | |
| QAL-IQA2025.07 | 0.971 | 0.973 | |
| LIQE2025.07 | 0.97 | 0.951 | |
| Re-IQA2025.07 | 0.97 | 0.971 | |
| LIQE2026.04 | 0.97 | 0.951 | |
| Re-IQANumber of trainable parameters=48M2026.05 | 0.97 | 0.971 | |
| LIQENumber of trainable parameters=151M2026.05 | 0.97 | 0.951 | |
| TReS2026.05 | 0.969 | 0.968 | |
| DAIQA2026.05 | 0.969 | 0.972 | |
| DB-CNN2026.04 | 0.968 | 0.971 | |
| DBCNN2026.05 | 0.968 | 0.971 | |
| HyperIQA2025.07 | 0.966 | 0.968 | |
| TreS2025.07 | 0.965 | 0.963 | |
| DBCNN2025.07 | 0.963 | 0.966 | |
| KGANet2025.07 | 0.963 | 0.966 | |
| HyperIQA2026.04 | 0.962 | 0.966 | |
| HyperIQA2026.05 | 0.962 | 0.966 | |
| UNIQUE2025.07 | 0.961 | 0.952 | |
| CONTRIQUE2025.07 | 0.96 | 0.961 | |
| MetaIQA2026.05 | 0.96 | 0.959 | |
| P2P-BM2026.05 | 0.959 | 0.958 | |
| QMambaNumber of trainable parameters=50M2026.05 | 0.959 | 0.958 | |
| dipIQ2025.07 | 0.94 | 0.933 | |
| MUSIQ2026.04 | 0.94 | 0.911 | |
| MUSIQ2026.05 | 0.94 | 0.911 | |
| CausalQualityBackbone=VGG2025.07 | 0.932 | 0.929 | |
| CausalQualityBackbone=EffNet2025.07 | 0.932 | 0.927 | |
| Ma192025.07 | 0.922 | 0.923 | |
| NIQE2025.07 | 0.908 | 0.904 | |
| ILNIQE2026.05 | 0.902 | 0.906 | |
| ILNIQE2025.07 | 0.887 | 0.894 | |
| TOPIQ-FR2025.07 | 0.887 | 0.882 |