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Video Quality Assessment On Youtube Ugc

Métriques

PLCC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
PLCC
Paper TitleRepository
SimpleVQA0.856A Deep Learning based No-reference Quality Assessment Model for UGC Videos-
HVS-5M0.8451HVS Revisited: A Comprehensive Video Quality Assessment Framework-
FAST-VQA (trained on LSVQ only)0.748FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling-
FasterVQA (fine-tuned)0.859Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment-
RAPIQUE0.7684RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content-
FAST-VQA (finetuned on YouTube-UGC)0.852FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling-
ReLaX-VQA (finetuned on YouTube-UGC)0.8652ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment-
VIDEVAL0.7733UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content-
CONTRIQUE0.813Image Quality Assessment using Contrastive Learning-
2BiVQA0.7942BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos-
DOVER (end-to-end)0.874Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives-
ChipQA0.6911ChipQA: No-Reference Video Quality Prediction via Space-Time Chips-
BVQA-20220.8178Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception-
DOVER (head-only)0.862Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives-
ReLaX-VQA (trained on LSVQ only)0.8354ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment-
ReLaX-VQA0.8204ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment-
CONVIQT0.822CONVIQT: Contrastive Video Quality Estimator-
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Video Quality Assessment On Youtube Ugc | SOTA | HyperAI