Video Quality Assessment On Msu Video Quality

KLCC

PLCC

SRCC

Type

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

比較表

モデル名	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
モデル 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