Video Quality Assessment On Msu Sr Qa Dataset

Metrics

KLCC

PLCC

SROCC

Type

Results

Performance results of various models on this benchmark

Model Name	KLCC	PLCC	SROCC	Type	Paper Title	Repository
3SSIM	0.16365	0.20138	0.21450	FR	-	-
ClipIQA+	0.69774	0.71808	0.56875	NR	Exploring CLIP for Assessing the Look and Feel of Images	-
MUSIQ trained on KONIQ	0.51897	0.59151	0.64589	NR	MUSIQ: Multi-scale Image Quality Transformer	-
FSIM	0.26942	0.35083	0.34996	FR	FSIM: A Feature Similarity Index for Image Quality Assessment	-
MSE	0.12067	0.09428	0.16441	FR	-	-
PieAPP	0.61945	0.75743	0.75215	FR	PieAPP: Perceptual Image-Error Assessment through Pairwise Preference	-
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	-
MS-SSIM	0.07821	0.16035	0.11017	FR	Multiscale structural similarity for image quality assessment
TOPIQ (IAA)	0.40663	0.51061	0.51687	NR	TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment	-
DBCNN	0.55139	0.63971	0.68621	NR	Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network	-
Q-Align (IQA)	0.61677	0.74116	0.75088	NR	Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels	-
LPIPS (Alex)	0.43158	0.52385	0.54461	FR	The Unreasonable Effectiveness of Deep Features as a Perceptual Metric	-
PSNR over Y	0.09998	0.13840	0.12914	FR	-	-
TOPIQ trained on PIPAL	0.42811	0.57564	0.55568	FR	TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment	-
LPIPS (VGG)	0.41471	0.52820	0.52868	FR	The Unreasonable Effectiveness of Deep Features as a Perceptual Metric	-
ERQA	0.47785	0.60188	0.59345	FR	ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution	-
AHIQ	0.47674	0.62311	0.60468	FR	-	-
VMAF	0.32283	0.40073	0.43219	FR	-	-
DISTS	0.42320	0.55042	0.53346	FR	Image Quality Assessment: Unifying Structure and Texture Similarity	-
Q-Align (IAA)	0.42211	0.50055	0.51521	NR	Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels	-

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