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Video Quality Assessment On Konvid 1K

评估指标

PLCC

评测结果

各个模型在此基准测试上的表现结果

模型名称
PLCC
Paper TitleRepository
VIDEVAL0.7803UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
RAPIQUE0.8175RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
FAST-VQA (trained on LSVQ only)0.855FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
2BiVQA0.8352BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos
ReLaX-VQA (trained on LSVQ only)0.8427ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
ChipQA0.7625ChipQA: No-Reference Video Quality Prediction via Space-Time Chips
FasterVQA (fine-tuned)0.898Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
HVS-5M0.8562HVS Revisited: A Comprehensive Video Quality Assessment Framework-
SimpleVQA0.860A Deep Learning based No-reference Quality Assessment Model for UGC Videos
DisCoVQA0.860DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
CONTRIQUE0.842Image Quality Assessment using Contrastive Learning
DOVER (end-to-end)0.905Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
VSFA0.7754Quality Assessment of In-the-Wild Videos
FAST-VQA (finetuned on KonViD-1k)0.892FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
CONVIQT0.849CONVIQT: Contrastive Video Quality Estimator
ReLaX-VQA0.8473ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
DOVER (head-only)0.894Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
TLVQM0.7688Two-Level Approach for No-Reference Consumer Video Quality Assessment
ReLaX-VQA (finetuned on KoNViD-1k)0.8668ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
PVQ0.770 Patch-VQ: 'Patching Up' the Video Quality Problem
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