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Image Quality Assessment On Msu Nr Vqa
Métriques
KLCC
PLCC
SRCC
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||||
|---|---|---|---|---|
| UNIQUE | 0.7648 | 0.9238 | 0.9148 | UNIQUE: Unsupervised Image Quality Estimation |
| LINEARITY | 0.7589 | 0.9106 | 0.9104 | Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment |
| MUSIQ | 0.7433 | 0.9068 | 0.9004 | MUSIQ: Multi-scale Image Quality Transformer |
| SPAQ MT-S | 0.7186 | 0.8814 | 0.8822 | Perceptual Quality Assessment of Smartphone Photography |
| SPAQ MT-A | 0.7148 | 0.8824 | 0.8794 | Perceptual Quality Assessment of Smartphone Photography |
| SPAQ BL | 0.7106 | 0.8855 | 0.8799 | Perceptual Quality Assessment of Smartphone Photography |
| PaQ-2-PiQ | 0.7079 | 0.8549 | 0.8705 | From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality |
| NIMA | 0.6745 | 0.8784 | 0.8494 | NIMA: Neural Image Assessment |
| KonCept512 | 0.6608 | 0.8464 | 0.8360 | KonIQ-10k: Towards an ecologically valid and large-scale IQA database |
| MEON | 0.3755 | 0.2898 | 0.5066 | - |
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