Video Quality Assessment On Msu Video Quality

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KLCC

PLCC

SRCC

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Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	KLCC	PLCC	SRCC	Type	Paper Title	Repository
LI	0.7640	0.9270	0.9131	NR	Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception	-
DOVER	0.7216	0.9099	0.8871	NR	Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives	-
NIMA	0.6745	0.8784	0.8494	NR	NIMA: Neural Image Assessment	-
SPAQ MT-S	0.7186	0.8814	0.8822	NR	Perceptual Quality Assessment of Smartphone Photography
VIDEVAL	0.5414	0.7717	0.7286	NR	UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content	-
FASTER-VQA	0.5645	0.8087	0.7508	NR	FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling	-
FAST-VQA	0.6498	0.8613	0.8308	NR	FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling	-
Y-NIQE	0.4215	0.6713	0.5985	NR	Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE	-
MDTVSFA	0.7883	0.9431	0.9289	NR	Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training	-
GVSP-UGCVQA-NR (multi_scale)	0.6942	0.8851	0.8673	NR	Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos	-
PaQ-2-PiQ	0.7079	0.8549	0.8705	NR	From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality	-
VSFA	0.7483	0.9180	0.9049	NR	Quality Assessment of In-the-Wild Videos	-
MEON	0.3775	0.2898	0.5066	NR	-	-
KonCept512	0.6608	0.8464	0.8360	NR	KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment	-
MUSIQ	0.7433	0.9068	0.9004	NR	MUSIQ: Multi-scale Image Quality Transformer	-
SPAQ MT-A	0.7148	0.8824	0.8794	NR	Perceptual Quality Assessment of Smartphone Photography
UNIQUE	0.7648	0.9238	0.9148	NR	UNIQUE: Unsupervised Image Quality Estimation	-
DBCNN	0.7750	0.9222	0.9220	NR	Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network	-
LINEARITY	0.7589	0.9106	0.9104	NR	Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment	-
GVSP-UGCVQA-NR (single_scale)	0.7037	0.8933	0.8742	NR	Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos	-

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