Video Quality Assessment On Msu Sr Qa Dataset

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					Paper Title
ClipIQA+	0.69774	0.71808	0.56875	NR	Exploring CLIP for Assessing the Look and Feel of Images
PieAPP	0.61945	0.75743	0.75215	FR	PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
Q-Align (IQA)	0.61677	0.74116	0.75088	NR	Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Q-Align (VQA)	0.58634	0.71121	0.71812	NR	Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
PaQ-2-PiQ	0.57753	0.70988	0.71167	NR	From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
MUSIQ trained on PaQ-2-PiQ	0.55312	0.66531	0.67746	NR	MUSIQ: Multi-scale Image Quality Transformer
DBCNN	0.55139	0.63971	0.68621	NR	Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
MANIQA	0.54744	0.62733	0.66613	NR	MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
TOPIQ trained on SPAQ (NR)	0.53140	0.60905	0.64923	NR	TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
MUSIQ trained on SPAQ	0.52673	0.60216	0.64927	NR	MUSIQ: Multi-scale Image Quality Transformer
ClipIQA+ ResNet50	0.52628	0.65154	0.65713	NR	Exploring CLIP for Assessing the Look and Feel of Images
Ma-Metric	0.52301	0.65357	0.67362	NR	Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Linearity (Norm-in-Norm Loss)	0.52172	0.62204	0.64382	NR	Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
MUSIQ trained on KONIQ	0.51897	0.59151	0.64589	NR	MUSIQ: Multi-scale Image Quality Transformer
TOPIQ	0.50670	0.57674	0.62715	NR	TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
ClipIQA	0.49417	0.58944	0.60808	NR	Exploring CLIP for Assessing the Look and Feel of Images
TReS trained on KONIQ	0.49004	0.56226	0.62578	NR	No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
TReS	0.48901	0.56277	0.62496	NR	No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
HyperIQA	0.48466	0.55211	0.59883	NR	Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network
TOPIQ FACE	0.48428	0.58949	0.59564	NR	TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment

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Video Quality Assessment On Msu Sr Qa Dataset

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