Video Quality Assessment On Msu Video Quality

KLCC

PLCC

SRCC

Type

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	KLCC	PLCC	SRCC	Type
blindly-assess-quality-of-in-the-wild-videos	0.7640	0.9270	0.9131	NR
disentangling-aesthetic-and-technical-effects	0.7216	0.9099	0.8871	NR
nima-neural-image-assessment	0.6745	0.8784	0.8494	NR
perceptual-quality-assessment-of-smartphone	0.7186	0.8814	0.8822	NR
ugc-vqa-benchmarking-blind-video-quality	0.5414	0.7717	0.7286	NR
fast-vqa-efficient-end-to-end-video-quality	0.5645	0.8087	0.7508	NR
fast-vqa-efficient-end-to-end-video-quality	0.6498	0.8613	0.8308	NR
barriers-towards-no-reference-metrics	0.4215	0.6713	0.5985	NR
unified-quality-assessment-of-in-the-wild	0.7883	0.9431	0.9289	NR
deep-learning-based-full-reference-and-no	0.6942	0.8851	0.8673	NR
from-patches-to-pictures-paq-2-piq-mapping	0.7079	0.8549	0.8705	NR
quality-assessment-of-in-the-wild-videos	0.7483	0.9180	0.9049	NR
Modèle 13	0.3775	0.2898	0.5066	NR
koniq-10k-an-ecologically-valid-database-for	0.6608	0.8464	0.8360	NR
musiq-multi-scale-image-quality-transformer	0.7433	0.9068	0.9004	NR
perceptual-quality-assessment-of-smartphone	0.7148	0.8824	0.8794	NR
unique-unsupervised-image-quality-estimation	0.7648	0.9238	0.9148	NR
blind-image-quality-assessment-using-a-deep	0.7750	0.9222	0.9220	NR
norm-in-norm-loss-with-faster-convergence-and	0.7589	0.9106	0.9104	NR
deep-learning-based-full-reference-and-no	0.7037	0.8933	0.8742	NR
perceptual-quality-assessment-of-smartphone	0.7106	0.8855	0.8799	NR