Visual Quality Evaluation on EBench-18K
0.9466SRCCReasonEdit
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| ReasonEditMethod Category=MLLM-based / Reward Model, Model Scale=4B2026.05 | 0.9466 | 0.8018 | 0.9524 | |
| ReasonEditMethod Category=MLLM-based / Reward Model, Model Scale=4B2026.05 | 0.9272 | 0.765 | 0.9311 | |
| LMM4Edit*Method Category=MLLM-based2026.05 | 0.9136 | 0.7432 | 0.9189 | |
| LMM4Edit*Method Category=MLLM-based2026.05 | 0.9048 | 0.7837 | 0.9176 | |
| InternVL2.5 (8B)*Method Category=MLLM-based, Model Scale=8B2026.05 | 0.8841 | 0.7571 | 0.8949 | |
| InternVL2.5 (8B)*Method Category=MLLM-based, Model Scale=8B2026.05 | 0.8836 | 0.7223 | 0.8861 | |
| DeepSeekVL2 (small)*Method Category=MLLM-based, Model Scale=small2026.05 | 0.8778 | 0.75 | 0.8819 | |
| DeepSeekVL2 (small)*Method Category=MLLM-based, Model Scale=small2026.05 | 0.8674 | 0.7303 | 0.8665 | |
| AHIQ*Method Category=Deep learning-based FR IQA2026.05 | 0.8365 | 0.6457 | 0.8515 | |
| AHIQ*Method Category=Deep learning-based FR IQA2026.05 | 0.8183 | 0.6241 | 0.8324 | |
| Q-Align*Method Category=Deep learning-based NR IQA2026.05 | 0.818 | 0.6285 | 0.8014 | |
| MANIQA*Method Category=Deep learning-based NR IQA2026.05 | 0.805 | 0.6136 | 0.8171 | |
| TOPIQ*Method Category=Deep learning-based NR IQA2026.05 | 0.7936 | 0.6021 | 0.8054 | |
| CVRKD*Method Category=Deep learning-based FR IQA2026.05 | 0.7935 | 0.5991 | 0.8106 | |
| CVRKD*Method Category=Deep learning-based FR IQA2026.05 | 0.7864 | 0.5917 | 0.8081 | |
| LPIPS (alex)Method Category=Deep learning-based FR IQA, Backbone=AlexNet2026.05 | 0.7395 | 0.5478 | 0.7594 | |
| LPIPS (vgg)Method Category=Deep learning-based FR IQA, Backbone=VGG2026.05 | 0.7248 | 0.5326 | 0.743 | |
| Q-Align*Method Category=Deep learning-based NR IQA2026.05 | 0.7046 | 0.5188 | 0.7321 | |
| Qwen2-VL (7B)Method Category=MLLM-based, Model Scale=7B2026.05 | 0.6786 | 0.4866 | 0.7041 | |
| MANIQA*Method Category=Deep learning-based NR IQA2026.05 | 0.6529 | 0.4716 | 0.7041 | |
| TOPIQ*Method Category=Deep learning-based NR IQA2026.05 | 0.632 | 0.6692 | 0.4565 | |
| SCSSIMMethod Category=Traditional FR IQA2026.05 | 0.5868 | 0.4149 | 0.5938 | |
| Qwen2-VL (7B)Method Category=MLLM-based, Model Scale=7B2026.05 | 0.5478 | 0.3981 | 0.52 | |
| GMSDMethod Category=Traditional FR IQA2026.05 | 0.5272 | 0.3689 | 0.5328 | |
| SSIMMethod Category=Traditional FR IQA2026.05 | 0.4635 | 0.3217 | 0.4865 | |
| ImageRewardMethod Category=Vision-language metrics2026.05 | 0.4033 | 0.2779 | 0.4662 | |
| ImageRewardMethod Category=Vision-language metrics2026.05 | 0.3991 | 0.2764 | 0.4351 | |
| BRISQUEMethod Category=Traditional NR IQA2026.05 | 0.3423 | 0.236 | 0.3955 | |
| VQAScoreMethod Category=Vision-language metrics2026.05 | 0.3014 | 0.205 | 0.3162 | |
| NIQEMethod Category=Traditional NR IQA2026.05 | 0.2979 | 0.2069 | 0.2453 | |
| PickScoreMethod Category=Vision-language metrics2026.05 | 0.2483 | 0.1666 | 0.2889 | |
| CLIPScoreMethod Category=Vision-language metrics2026.05 | 0.2325 | 0.1586 | 0.2581 | |
| VQAScoreMethod Category=Vision-language metrics2026.05 | 0.2185 | 0.1444 | 0.2537 | |
| CLIPScoreMethod Category=Vision-language metrics2026.05 | 0.2181 | 0.1467 | 0.2243 | |
| LPIPS (alex)Method Category=Deep learning-based FR IQA, Backbone=AlexNet2026.05 | 0.1832 | 0.1234 | 0.2782 | |
| NIQEMethod Category=Traditional NR IQA2026.05 | 0.1748 | 0.12 | 0.1926 | |
| LPIPS (vgg)Method Category=Deep learning-based FR IQA, Backbone=VGG2026.05 | 0.1643 | 0.1101 | 0.1902 | |
| DIIVINEMethod Category=Traditional NR IQA2026.05 | 0.1429 | 0.0929 | 0.3555 | |
| PickScoreMethod Category=Vision-language metrics2026.05 | 0.1357 | 0.0874 | 0.2046 | |
| BRISQUEMethod Category=Traditional NR IQA2026.05 | 0.1302 | 0.0883 | 0.2095 | |
| SCSSIMMethod Category=Traditional FR IQA2026.05 | 0.064 | 0.0433 | 0.2646 | |
| DIIVINEMethod Category=Traditional NR IQA2026.05 | 0.0121 | 0.0083 | 0.2051 | |
| GMSDMethod Category=Traditional FR IQA2026.05 | 0.0099 | 0.0063 | 0.0959 | |
| SSIMMethod Category=Traditional FR IQA2026.05 | 0.0035 | 0.0007 | 0.2133 |