Image Quality Assessment On Msu Nr Vqa
Metrics
KLCC
PLCC
SRCC
Results
Performance results of various models on this benchmark
Model Name | KLCC | PLCC | SRCC | Paper Title | Repository |
|---|---|---|---|---|---|
| LINEARITY | 0.7589 | 0.9106 | 0.9104 | Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment | |
| 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 | |
| KonCept512 | 0.6608 | 0.8464 | 0.8360 | KonIQ-10k: Towards an ecologically valid and large-scale IQA database | |
| NIMA | 0.6745 | 0.8784 | 0.8494 | NIMA: Neural Image Assessment | |
| UNIQUE | 0.7648 | 0.9238 | 0.9148 | UNIQUE: Unsupervised Image Quality Estimation | |
| SPAQ MT-A | 0.7148 | 0.8824 | 0.8794 | Perceptual Quality Assessment of Smartphone Photography | |
| MEON | 0.3755 | 0.2898 | 0.5066 | - | - |
| 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 |
0 of 10 row(s) selected.