Video Quality Assessment on DIVIDE-3k
0.8442SROCCDOVER++
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| DOVER++Pre-training Dataset=LSVQ [1]2022.11 | 0.8442 | 0.8537 | 0.6603 | |
| DOVERPre-training Dataset=LSVQ [1]2022.11 | 0.8331 | 0.8438 | 0.648 | |
| FAST-VQAPre-training Dataset=LSVQ [1]2022.11 | 0.8184 | 0.8288 | 0.6285 | |
| Li et al.Pre-training Dataset=fused ([15, 73-75])2022.11 | 0.7967 | 0.8125 | 0.6138 | |
| DOVERTraining on=LSVQ [1]2022.11 | 0.7727 | 0.7806 | 0.5799 | |
| UNIQUEPre-training Dataset=fused ([15, 73-75])2022.11 | 0.7529 | 0.7637 | 0.5634 | |
| MDTVSFAPre-training Dataset=NA2022.11 | 0.7522 | 0.7409 | 0.5647 | |
| RAPIQUEPre-training Dataset=handcraft + KoNiQ [73]2022.11 | 0.7341 | 0.7547 | 0.5498 | |
| Li et al.Training on=LSVQ [1]2022.11 | 0.7318 | 0.7524 | 0.5395 | |
| VSFAPre-training Dataset=NA2022.11 | 0.7254 | 0.7386 | 0.5395 | |
| VIDEVALPre-training Dataset=NA (pure handcraft)2022.11 | 0.7056 | 0.7162 | 0.5233 | |
| BVQITraining on=CLIP [77]2022.11 | 0.6678 | 0.6802 | 0.4842 | |
| TLVQMPre-training Dataset=NA (pure handcraft)2022.11 | 0.6461 | 0.6807 | 0.4699 | |
| Patch-VQTraining on=LSVQ [1]2022.11 | 0.6454 | 0.6713 | 0.4489 | |
| CLIP-IQATraining on=CLIP [77]2022.11 | 0.5882 | 0.591 | 0.4067 | |
| TPQITraining on=None2022.11 | 0.4407 | 0.4432 | 0.3045 | |
| NIQETraining on=None2022.11 | 0.3524 | 0.3839 | 0.2634 |