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プラットフォーム
ホーム
SOTA
ビデオ品質評価
Video Quality Assessment On Konvid 1K
Video Quality Assessment On Konvid 1K
評価指標
PLCC
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
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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