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Video Quality Assessment
Video Quality Assessment On Konvid 1K
Video Quality Assessment On Konvid 1K
Metrics
PLCC
Results
Performance results of various models on this benchmark
Columns
Model Name
PLCC
Paper Title
DOVER (end-to-end)
0.905
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
FasterVQA (fine-tuned)
0.898
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
DOVER (head-only)
0.894
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
FAST-VQA (finetuned on KonViD-1k)
0.892
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
ReLaX-VQA (finetuned on KoNViD-1k)
0.8668
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
SimpleVQA
0.860
A Deep Learning based No-reference Quality Assessment Model for UGC Videos
DisCoVQA
0.860
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
HVS-5M
0.8562
HVS Revisited: A Comprehensive Video Quality Assessment Framework
FAST-VQA (trained on LSVQ only)
0.855
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
CONVIQT
0.849
CONVIQT: Contrastive Video Quality Estimator
ReLaX-VQA
0.8473
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
ReLaX-VQA (trained on LSVQ only)
0.8427
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
CONTRIQUE
0.842
Image Quality Assessment using Contrastive Learning
2BiVQA
0.835
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos
BVQA-2022
0.834
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
RAPIQUE
0.8175
RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
VIDEVAL
0.7803
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
VSFA
0.7754
Quality Assessment of In-the-Wild Videos
PVQ
0.770
Patch-VQ: 'Patching Up' the Video Quality Problem
TLVQM
0.7688
Two-Level Approach for No-Reference Consumer Video Quality Assessment
0 of 21 row(s) selected.
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