No-Reference Quality Evaluation for Gamut Mapping Images Based on Natural Scene Statistics

Wei Yu; Jingjing Xu; Yuying Liu; Junsheng Zhang; Tengteng Li

doi:10.3788/LOP57.141006

Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141006(2020)

No-Reference Quality Evaluation for Gamut Mapping Images Based on Natural Scene Statistics

Wei Yu¹, Jingjing Xu², Yuying Liu^2、*, Junsheng Zhang², and Tengteng Li²

Author Affiliations

¹Engineering & Technical College of Chengdu University of Technology, Leshan, Sichuan 614000, China;

²School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

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Figures & Tables(13)

Fig. 1. Framework of our algorithm

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Fig. 2. Original image and gamut mapping images with decreasing quality. (a) Original image; (b) gamut mapping image1; (c) gamut mapping image2; (d) gamut mapping image3

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Fig. 3. Changes in the frequency domain moment and entropy of the image with frequency. (a) Change of image frequency domain entropy with frequency; (b) change of image frequency domain mean with frequency; (c) change of image frequency domain standard deviation with frequency

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Fig. 4. Empirical histograms of relative chroma

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Fig. 5. Empirical histograms of relative hue

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Fig. 6. Empirical histogram of relative hue and chroma

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Fig. 7. Structure of the AdaBoosting BPNN; (a) Structure of the AdaBoosting algorithm; (b) structure of BPNN

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Fig. 8. Performance comparison of grayscale features and color features. (a) BS database; (b) IG database; (c) LC database

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Fig. 9. Influence of peak value features on algorithm performance. (a) BS database; (b) IG database; (c) LC database

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Fig. 10. Example of gamut mapping images. (a) MOS is 0.6268; (b) MOS is 0.5972; (c) MOS is 0.2927; (d) MOS is 0.1341

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Table 1. Gamut mapping image quality evaluation databases
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Table 1. Gamut mapping image quality evaluation databases
Database Referenceimage Distortedimage Evaluation GAM
BS 97 1067 5199 11
IG 65 520 3698 8
LC 72 576 5209 8

Table 2. Performance comparison of different algorithms in three databases

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Table 2. Performance comparison of different algorithms in three databases

Algorithm	BS				IG				LC
Algorithm	PLCC	SRCC	KRCC	RMSE	PLCC	SRCC	KRCC	RMSE	PLCC	SRCC	KRCC	RMSE
BRISQUE	0.7633	0.5678	0.4126	0.4386	0.5153	0.4654	0.3345	0.4739	0.5026	0.5274	0.3802	0.4229
BIQI	0.6188	0.4422	0.3135	0.5335	0.3680	0.3078	0.2227	0.5115	0.3777	0.3521	0.2486	0.4516
DESIQUE	0.8213	0.5941	0.4354	0.3878	0.5987	0.5666	0.4211	0.4440	0.5692	0.5973	0.4429	0.4367
DIIVINE	0.7339	0.5457	0.3949	0.4603	0.4289	0.3694	0.2694	0.4986	0.4210	0.4111	0.2920	0.4441
NFERM	0.7441	0.5556	0.4072	0.4539	0.4399	0.4150	0.2968	0.4942	0.4934	0.4985	0.3617	0.4263
BLIINDS_II	0.7081	0.5499	0.4031	0.4777	0.3646	0.3102	0.2184	0.5127	0.4274	0.3323	0.2370	0.4449
IDEAL	0.7859	0.6652	0.4994	0.4173	0.6195	0.6139	0.4550	0.4327	0.5780	0.5989	0.4417	0.3977
IL_NIQE	0.5545	0.4849	0.3937	0.4923	0.3560	0.3416	0.2808	0.4019	0.4748	0.3459	0.3439	0.3842
NIQE	0.5840	0.4479	0.3840	0.5132	0.3724	0.3667	0.2936	0.4675	0.4748	0.3247	0.2418	0.3458
Ours	0.8170	0.6774	0.5100	0.3918	0.7369	0.7086	0.5526	0.3773	0.6256	0.6154	0.4630	0.3849

Table 3. Predicted results by different algorithms

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Table 3. Predicted results by different algorithms

Image	MOS	DESIQUE	BRISQUE	IL_NIQE	IDEAL	Ours
Fig.10(a)	0.6268	21.2382	18.4999	20.5977	4.6353	0.6736
Fig.10(b)	0.5927	21.4467	20.2568	22.5301	4.7215	0.6213
Fig.10(c)	0.2927	22.1088	20.7486	22.2513	4.3958	0.3042
Fig.10(d)	0.1341	20.5985	26.2595	22.3621	4.1315	0.1556

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Wei Yu, Jingjing Xu, Yuying Liu, Junsheng Zhang, Tengteng Li. No-Reference Quality Evaluation for Gamut Mapping Images Based on Natural Scene Statistics[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141006

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