Video Quality Assessment On Youtube Ugc
Métriques
PLCC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | PLCC |
|---|---|
| a-deep-learning-based-no-reference-quality | 0.856 |
| hvs-revisited-a-comprehensive-video-quality | 0.8451 |
| fast-vqa-efficient-end-to-end-video-quality | 0.748 |
| neighbourhood-representative-sampling-for | 0.859 |
| rapique-rapid-and-accurate-video-quality | 0.7684 |
| fast-vqa-efficient-end-to-end-video-quality | 0.852 |
| relax-vqa-residual-fragment-and-layer-stack | 0.8652 |
| ugc-vqa-benchmarking-blind-video-quality | 0.7733 |
| image-quality-assessment-using-contrastive | 0.813 |
| 2bivqa-double-bi-lstm-based-video-quality | 0.794 |
| disentangling-aesthetic-and-technical-effects | 0.874 |
| chipqa-no-reference-video-quality-prediction | 0.6911 |
| blindly-assess-quality-of-in-the-wild-videos | 0.8178 |
| disentangling-aesthetic-and-technical-effects | 0.862 |
| relax-vqa-residual-fragment-and-layer-stack | 0.8354 |
| relax-vqa-residual-fragment-and-layer-stack | 0.8204 |
| conviqt-contrastive-video-quality-estimator | 0.822 |