Video Quality Assessment On Msu Sr Qa Dataset

Metriken

KLCC

PLCC

SROCC

Type

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle

Modellname	KLCC	PLCC	SROCC	Type
Modell 1	0.16365	0.20138	0.21450	FR
exploring-clip-for-assessing-the-look-and	0.69774	0.71808	0.56875	NR
musiq-multi-scale-image-quality-transformer	0.51897	0.59151	0.64589	NR
fsim-a-feature-similarity-index-for-image	0.26942	0.35083	0.34996	FR
Modell 5	0.12067	0.09428	0.16441	FR
pieapp-perceptual-image-error-assessment	0.61945	0.75743	0.75215	FR
norm-in-norm-loss-with-faster-convergence-and	0.52172	0.62204	0.64382	NR
multiscale-structural-similarity-for-image	0.07821	0.16035	0.11017	FR
topiq-a-top-down-approach-from-semantics-to	0.40663	0.51061	0.51687	NR
blind-image-quality-assessment-using-a-deep	0.55139	0.63971	0.68621	NR
q-align-teaching-lmms-for-visual-scoring-via	0.61677	0.74116	0.75088	NR
the-unreasonable-effectiveness-of-deep	0.43158	0.52385	0.54461	FR
Modell 13	0.09998	0.13840	0.12914	FR
topiq-a-top-down-approach-from-semantics-to	0.42811	0.57564	0.55568	FR
the-unreasonable-effectiveness-of-deep	0.41471	0.52820	0.52868	FR
erqa-edge-restoration-quality-assessment-for	0.47785	0.60188	0.59345	FR
Modell 17	0.47674	0.62311	0.60468	FR
Modell 18	0.32283	0.40073	0.43219	FR
image-quality-assessment-unifying-structure	0.42320	0.55042	0.53346	FR
q-align-teaching-lmms-for-visual-scoring-via	0.42211	0.50055	0.51521	NR
Modell 21	0.34004	0.41892	0.44064	NR
blindly-assess-image-quality-in-the-wild	0.48466	0.55211	0.59883	NR
Modell 23	0.32331	0.39744	0.43296	FR
Modell 24	0.13551	0.19672	0.17889	FR
unified-quality-assessment-of-in-the-wild	0.48406	0.61821	0.60193	NR
quality-assessment-of-in-the-wild-videos	0.43634	0.54407	0.53652	NR
musiq-multi-scale-image-quality-transformer	0.44669	0.52404	0.56152	NR
Modell 28	0.24254	0.33169	0.33167	NR
musiq-multi-scale-image-quality-transformer	0.52673	0.60216	0.64927	NR
multiscale-structural-similarity-for-image	0.18174	0.21800	0.24422	FR
topiq-a-top-down-approach-from-semantics-to	0.53140	0.60905	0.64923	NR
exploring-clip-for-assessing-the-look-and	0.38794	0.50379	0.49881	NR
no-reference-image-quality-assessment-via-1	0.39398	0.50005	0.48882	NR
topiq-a-top-down-approach-from-semantics-to	0.28473	0.34000	0.36204	NR
topiq-a-top-down-approach-from-semantics-to	0.48428	0.58949	0.59564	NR
multiscale-structural-similarity-for-image	0.16578	0.30014	0.21604	FR
the-2018-pirm-challenge-on-perceptual-image	0.39101	0.53178	0.52319	NR
musiq-multi-scale-image-quality-transformer	0.55312	0.66531	0.67746	NR
from-patches-to-pictures-paq-2-piq-mapping	0.57753	0.70988	0.71167	NR
vila-learning-image-aesthetics-from-user	0.26180	0.28846	0.33728	NR
exploring-clip-for-assessing-the-look-and	0.49417	0.58944	0.60808	NR
no-reference-image-quality-assessment-via-1	0.49004	0.56226	0.62578	NR
no-reference-image-quality-assessment-in-the	0.24803	0.31143	0.32327	NR
image-quality-assessment-from-error	0.17175	0.20670	0.22468	FR
Modell 45	0.08263	0.10931	0.10733	FR
topiq-a-top-down-approach-from-semantics-to	0.46217	0.57955	0.57341	FR
maniqa-multi-dimension-attention-network-for	0.54744	0.62733	0.66613	NR
exploring-clip-for-assessing-the-look-and	0.52628	0.65154	0.65713	NR
Modell 49	0.11040	0.14638	0.14277	FR
Modell 50	0.26485	0.36944	0.34862	FR
topiq-a-top-down-approach-from-semantics-to	0.50670	0.57674	0.62715	NR
multiscale-structural-similarity-for-image	0.17468	0.20935	0.23108	FR
shift-tolerant-perceptual-similarity-metric-1	0.42897	0.54740	0.53473	FR
learning-a-no-reference-quality-metric-for	0.52301	0.65357	0.67362	NR
q-align-teaching-lmms-for-visual-scoring-via	0.58634	0.71121	0.71812	NR
shift-tolerant-perceptual-similarity-metric-1	0.45898	0.56431	0.57336	FR
nima-neural-image-assessment	0.20377	0.26550	0.25887	NR
no-reference-image-quality-assessment-via-1	0.48901	0.56277	0.62496	NR
topiq-a-top-down-approach-from-semantics-to	0.26774	0.33940	0.34092	NR
locally-adaptive-structure-and-texture	0.41261	0.53289	0.51717	FR