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Video Quality Assessment On Msu Sr Qa Dataset

Metrics

KLCC
PLCC
SROCC
Type

Results

Performance results of various models on this benchmark

Paper TitleRepository
ClipIQA+0.697740.718080.56875NRExploring CLIP for Assessing the Look and Feel of Images
PieAPP0.619450.757430.75215FRPieAPP: Perceptual Image-Error Assessment through Pairwise Preference
Q-Align (IQA)0.616770.741160.75088NRQ-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Q-Align (VQA)0.586340.711210.71812NRQ-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
PaQ-2-PiQ0.577530.709880.71167NRFrom Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
MUSIQ trained on PaQ-2-PiQ0.553120.665310.67746NRMUSIQ: Multi-scale Image Quality Transformer
DBCNN0.551390.639710.68621NRBlind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
MANIQA0.547440.627330.66613NRMANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
TOPIQ trained on SPAQ (NR)0.531400.609050.64923NRTOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
MUSIQ trained on SPAQ0.526730.602160.64927NRMUSIQ: Multi-scale Image Quality Transformer
ClipIQA+ ResNet500.526280.651540.65713NRExploring CLIP for Assessing the Look and Feel of Images
Ma-Metric0.523010.653570.67362NRLearning a No-Reference Quality Metric for Single-Image Super-Resolution
Linearity (Norm-in-Norm Loss)0.521720.622040.64382NRNorm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
MUSIQ trained on KONIQ0.518970.591510.64589NRMUSIQ: Multi-scale Image Quality Transformer
TOPIQ0.506700.576740.62715NRTOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
ClipIQA0.494170.589440.60808NRExploring CLIP for Assessing the Look and Feel of Images
TReS trained on KONIQ0.490040.562260.62578NRNo-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
TReS0.489010.562770.62496NRNo-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
HyperIQA0.484660.552110.59883NRBlindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network-
TOPIQ FACE0.484280.589490.59564NRTOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
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Video Quality Assessment On Msu Sr Qa Dataset | SOTA | HyperAI