Image Quality Assessment on AGIQA
0.842SRCCMG-IQA
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| MG-IQAmulti-dataset training=true, training_data=KADID-10k + SPAQ2026.04 | 0.842 | 0.858 | |
| Q-ProbeCategory=MLLMs-based2026.01 | 0.837 | 0.813 | |
| VQ-R1multi-dataset training=true, training_data=KADID-10k + SPAQ2026.04 | 0.813 | 0.831 | |
| VisualQuality-R1Category=MLLMs-based2026.01 | 0.805 | 0.843 | |
| Q-Insightmulti-dataset training=true, training_data=KADID-10k + SPAQ2026.04 | 0.772 | 0.79 | |
| Q-DeepSight†Category=MLLM (w/ reasoning), Training Dataset=KonIQ + DQ-7K2026.04 | 0.768 | 0.822 | |
| VisualQuality-R1Category=MLLM-based, Training Dataset=KonIQ2025.10 | 0.767 | 0.822 | |
| Q-InsightCategory=MLLM (w/ reasoning), Training Dataset=KonIQ2026.04 | 0.766 | 0.816 | |
| Zoom-IQACategory=MLLM (w/ reasoning), Training Dataset=KonIQ2026.04 | 0.765 | 0.816 | |
| Q-InsightCategory=MLLM-based, Training Dataset=KonIQ2025.10 | 0.764 | 0.811 | |
| Q-Insight†Category=MLLM (w/ reasoning), Training Dataset=KonIQ + DQ-7K2026.04 | 0.764 | 0.811 | |
| VisualQuality-R1Category=MLLM (w/ reasoning), Training Dataset=KonIQ2026.04 | 0.76 | 0.817 | |
| Q-DeepSightCategory=MLLM (w/ reasoning), Training Dataset=KonIQ2026.04 | 0.757 | 0.815 | |
| Q-InsightCategory=MLLMs-based2026.01 | 0.749 | 0.794 | |
| Qwen-SFTCategory=MLLM (w/ reasoning), Training Dataset=KonIQ2026.04 | 0.739 | 0.813 | |
| DeQA-ScoreCategory=MLLMs-based2026.01 | 0.738 | 0.743 | |
| Qwen2.5-VL-7BCategory=MLLMs-based2026.01 | 0.735 | 0.772 | |
| Q-AlignCategory=MLLM-based, Training Dataset=KonIQ2025.10 | 0.735 | 0.772 | |
| Q-AlignCategory=MLLM (w/o reasoning), Training Dataset=KonIQ2026.04 | 0.735 | 0.772 | |
| DeQACategory=MLLM-based, Training Dataset=KonIQ2025.10 | 0.729 | 0.809 | |
| DeQA-ScoreCategory=MLLM (w/o reasoning), Training Dataset=KonIQ2026.04 | 0.729 | 0.809 | |
| ManIQACategory=Non-MLLM Deep-learning, Training Dataset=KonIQ2025.10 | 0.723 | 0.685 | |
| UnifiedReward-TCategory=MLLMs-based2026.01 | 0.722 | 0.745 | |
| RALICategory=Non-MLLM Deep-learning, Training Dataset=KonIQ2025.10 | 0.715 | 0.779 | |
| LIQECategory=Non-MLLM Deep-learning, Training Dataset=KonIQ2025.10 | 0.697 | 0.739 | |
| CLIP-IQA+Category=MLLM (w/o reasoning), Training Dataset=KonIQ2026.04 | 0.685 | 0.736 | |
| Q-AlignCategory=MLLMs-based2026.01 | 0.682 | 0.694 | |
| C2ScoreCategory=MLLM-based, Training Dataset=KonIQ2025.10 | 0.671 | 0.777 | |
| C2ScoreCategory=MLLM (w/o reasoning), Training Dataset=KonIQ2026.04 | 0.671 | 0.777 | |
| LIQECategory=MLLMs-based2026.01 | 0.653 | 0.653 | |
| DBCNNCategory=Non-MLLM, Training Dataset=KonIQ2026.04 | 0.641 | 0.73 | |
| HyperIQACategory=Non-MLLM, Training Dataset=KonIQ2026.04 | 0.64 | 0.702 | |
| CLIP-IQA+Category=Non-MLLM Deep-learning, Training Dataset=KonIQ2025.10 | 0.636 | 0.736 | |
| ManIQACategory=Non-MLLM, Training Dataset=KonIQ2026.04 | 0.636 | 0.723 | |
| MUSIQCategory=Non-MLLM Deep-learning, Training Dataset=KonIQ2025.10 | 0.63 | 0.722 | |
| MUSIQCategory=Non-MLLM, Training Dataset=KonIQ2026.04 | 0.63 | 0.722 | |
| UNIQUECategory=Deep Learning2026.01 | 0.608 | 0.581 | |
| NIQECategory=Handcrafted2026.01 | 0.533 | 0.56 | |
| NIQECategory=Handcrafted, Training Dataset=N/A2025.10 | 0.533 | 0.56 | |
| BRISQUECategory=Handcrafted2026.01 | 0.497 | 0.541 | |
| BRISQUECategory=Handcrafted, Training Dataset=N/A2025.10 | 0.497 | 0.541 | |
| MUSIQCategory=Deep Learning2026.01 | 0.494 | 0.434 | |
| MANIQACategory=Deep Learning2026.01 | 0.422 | 0.448 |