Objective image quality assessment based on image color appearance and gradient features

Shi Chen-Yang; Lin Yan-Dan

doi:10.7498/aps.69.20200753

Abstract

With the rapid development of color image contents and imaging devices in various kinds of multimedia communication systems, conventional grayscale counterparts are replaced by chromatic ones. Under such a transition, the image quality assessment (IQA) model needs to be built by subjective visual measurement, designed in accordance with the results, and applied to the related practical problems. Based on the visual perception characteristics, chromaticity and the structure feature information are quantified, and an objective IQA model combining the color appearance and the gradient image features is proposed in this paper, namely color appearance and gradient similarity(CAGS) model. Two new color appearance indices, vividness and depth, are selected to build the chromatic similarity map. The structure information is characterized by gradient similarity map. Vividness map plays two roles in the proposed model. One is utilized as feature extractor to compute the local quality of distorted image, and the other is as a weight part to reflect the importance of local domain. To quantify the specific parameters of CAGS, Taguchi method is used and four main parameters, i.e., K_V, K_D, K_G and α, of this model are determined based on the statistical correlation indices. The optimal parameters of CAGS are K_V = K_D = 0.02, K_G = 50, and α = 0.1. Furthermore, the CAGS is tested by utilizing 94 reference images and 4830 distorted images from the four open image databases (LIVE, CSIQ, TID2013 and IVC). Additionally, the influences of the 35 distortion types on IQA are analyzed. Massive experiments are performed on four publicly available benchmark databases between CAGS and other 10 state-of-the-art and recently published IQA models, for the accuracy, complexity and generalization performance of IQA. The experimental results show that the accuracy PLCC of the CAGS model can achieve 0.8455 at lowest and 0.9640 at most in the four databases, and the results about commonly evaluation criteria prove that the CAGS performs higher consistency with the subjective evaluations. Among the 35 distortion types, the two distortion types, namely contrast change and change of color saturation, CAGS and mostly IQA models have the worst influence on IQA, and the CAGS yields the highest top three rank number. Moreover, the SROCC values of CAGS for other distortion types are all larger than 0.6 and the number of SROCC value larger than 0.95 is 14 times. Besides, the CAGS maintains a moderate computational complexity. These results of test and comparison above show that the CAGS model is effective and feasible, and the corresponding model has an excellent performance.

Keywords:

Authors and contacts

Shi Chen-Yang¹,
Lin Yan-Dan^1,2

1.
Department of Light Sources and Illuminating Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China
2.
Institute of Future Lighting, Academy for Engineering & Technology, Fudan university, Shanghai 200433, China

Corresponding author: Lin Yan-Dan, ydlin@fudan.edu.cn

Funds: Project supported by the National Key R&D Program of China (Grant No. 2017YFB0403700)

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References

[1]	姚军财, 刘贵忠 2018 物理学报 67 108702 Google Scholar Yao J C, Liu G Z 2018 Acta Phys. Sin. 67 108702 Google Scholar
[2]	Athar S, Wang Z 2019 IEEE Access 7 140030 Google Scholar
[3]	Lin W S, Kuo C C J 2011 J. Vis. Commun. Image R. 22 297 Google Scholar
[4]	Chang H W, Zhang Q W, Wu Q G, Gan Y 2015 Neurocomputing 151 1142 Google Scholar
[5]	Wang Z, Bovik A C, Sheikh H R, Simoncelli E P 2004 IEEE Trans. Image Process. 13 600 Google Scholar
[6]	Sheikh H R, Bovik A C 2006 IEEE Trans. Image Process. 15 430 Google Scholar
[7]	Sheikh H R, Bovik A C, de Veciana G 2005 IEEE Trans. Image Process. 14 2117 Google Scholar
[8]	Wang Z, Simoncelli E P, Bovik A C 2003 37^th Asilomar Conference on Signals, Systems and Computer PacificGrove, CA, November 9−12, 2003 pp1398−1402
[9]	Wang Z, Li Q 2011 IEEE Trans. Image Process. 20 1185 Google Scholar
[10]	Larson E C, Chandler D M 2010 J. Electron. Imaging 19 011006 Google Scholar
[11]	Zhang L, Zhang L, Mou X Q 2010 IEEE International Conference on Image Processing Hong Kong, Peoples of China, September 26−29, 2010 pp321−324
[12]	Zhang L, Zhang L, Mou X Q, Zhang D 2011 IEEE Trans. Image Process. 20 2378 Google Scholar
[13]	Liu A M, Lin W S, Narwaria M 2012 IEEE Trans. Image Process. 21 1500 Google Scholar
[14]	Jia H Z, Zhang L, Wang T H 2018 IEEE Access 6 65885 Google Scholar
[15]	姚军财, 申静 2020 物理学报 69 148702 Google Scholar Yao J C, Shen J 2020 Acta Phys. Sin. 69 148702 Google Scholar
[16]	Robertson A R 1990 Color Res. Appl. 15 167 Google Scholar
[17]	Mahny M, Vaneycken L, Oosterlinck A 1994 Color Res. Appl. 19 105
[18]	Lee D, Plataniotis K N 2015 IEEE Trans. Image Process. 24 3950 Google Scholar
[19]	Lee D, Plataniotis K N 2014 2014 International Conference on Acoustics, Speech and Signal Processing (ICASSP) Florence, Italy, May 4−9, 2014 pp166−170
[20]	Berns R S 2014 Color Res. Appl. 39 322 Google Scholar
[21]	Zhang L, Shen Y, Li H Y 2014 IEEE Trans. Image Process. 23 4270 Google Scholar
[22]	Jain R C, Kasturi R, Schunck B G 1995 Machine Vision (New York: McGraw-Hill) pp140–185
[23]	Sonka M, Hlavac V, Boyle R 2008 Image Processing, Analysis and Machine Vision (3rd Ed.) (Stanford: Cengage Learning) p77
[24]	Xue W F, Zhang L, Mou X Q, Bovik A C 2014 IEEE Trans. Image Process. 23 684 Google Scholar
[25]	Kim D O, Han H S, Park R H 2010 IEEE Trans. Consum. Electr. 56 930 Google Scholar
[26]	Nafchi H Z, Shahkolaei A, Hedjam R, Cheriet M 2016 IEEE Access 4 5579 Google Scholar
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Cited By

图 1 颜色1和2的vividness($ V_{ab}^* $)和depth($ D_{ab}^* $)维度表征, 线段长度定义对应属性^[20]

Figure 1. Dimensions of vividness, $ V_{ab}^* $, and depth, $ D_{ab}^* $ for colors 1 and 2. Line lengths define each attribute^[20].

DownLoad: Full-Size Img PowerPoint

图 2 从LIVE数据库中提取的典型图像　(a)为参考图像; (b)为高斯模糊畸变类型的失真图像; (c)和(e)分别是参考图像的Vividness和Depth图; (d)和(f)分别是失真图像的Vividness和Depth图; (g)是色貌相似图; (h)为梯度相似图

Figure 2. Typical images extracted from LIVE: (a) The reference image;(b) the distorted vision of it by Gaussian blur distortion type; (c) and (e) are the vividness and depth map of the reference image, respectively; (d) and (f) are the vividness and depth map of the distorted image; (g) the color appearance similarity map by connecting the vividness and depth similarity map; (h) the gradient similarity map.

DownLoad: Full-Size Img PowerPoint

图 3 本文提出的IQA模型CAGS的计算过程

Figure 3. Illustration for the computational process of the proposed IQA model CAGS.

DownLoad: Full-Size Img PowerPoint

图 4 SROCC和RMSE对应的不同水准的S/N值

Figure 4. S/N ratio of different levels for SROCC and RMSE.

DownLoad: Full-Size Img PowerPoint

图 5 不同K_G值对模型性能的影响

Figure 5. Performance of different K_G values.

DownLoad: Full-Size Img PowerPoint

图 6 基于TID2013数据库的主观MOS与IQA模型计算结果拟合对比　(a) IW-SSIM; (b) IFC; (c) VIF; (d) MAD; (e) RFSIM; (f) FSIMc; (g) GSM; (h) CVSS; (i) CAGS

Figure 6. Scatter plots of subjective MOS against scores calculated by IQA models’ prediction for TID2013 databases: (a) IW-SSIM; (b) IFC; (c) VIF; (d) MAD; (e) RFSIM; (f) FSIMc; (g) GSM; (h) CVSS; (i) CAGS.

DownLoad: Full-Size Img PowerPoint

图 7 CAGS与MPCC在TID2013数据库种不同失真类型PLCC值对比

Figure 7. PLCC comparison of different distortion types between CAGS and MPCC on TID2013.

DownLoad: Full-Size Img PowerPoint

表 1 IQA数据库基本信息

Table 1. Benchmark test databases for IQA.

数据库	原始图像数量	失真图像数量	失真类型	观察者
TID2013	25	3000	24	971
CSIQ	30	866	6	35
LIVE	29	779	5	161
IVC	10	185	4	15

DownLoad: CSV

表 2 变量参数及其控制水准

Table 2. Influence factors and level setting for CAGS.

代号	参数表述	水准数	水准一	水准二	水准三
A	K_V	3	0.002	0.02	0.2
B	K_D	3	0.002	0.02	0.2
C	K_G	3	10	50	100
D	α	3	0.1	0.5	1

DownLoad: CSV

表 3 采用L₉(3⁴)直交表的实验设计及IVC数据库测试结果

Table 3. Design with a L₉(3⁴) orthogonal array for IVC database.

实验序号	A	B	C	D	SROCC	SROCC的S/N	RMSE	RMSE的S/N
1	1	1	1	1	0.9300	–0.6303	0.4113	7.7168
2	1	2	2	2	0.9192	–0.7318	0.4533	6.8723
3	1	3	3	3	0.9096	–0.8230	0.4825	6.3301
4	2	1	2	3	0.9171	–0.7517	0.4596	6.7524
5	2	2	3	1	0.9173	–0.7498	0.4672	6.6099
6	2	3	1	2	0.9291	–0.6388	0.4142	7.6558
7	3	1	3	2	0.9114	–0.8058	0.4735	6.4936
8	3	2	1	3	0.9279	–0.6500	0.4174	7.5890
9	3	3	2	1	0.9195	–0.7290	0.4481	6.9725

DownLoad: CSV

表 4 对比不同IQA模型的4个数据库性能

Table 4. Performance comparison of IQA models on four databases.

数据库		SSIM	IW-SSIM	IFC	VIF	MAD	RFSIM	FSIM_C	GSM	CVSS	MPCC	Proposed
TID2013	SROCC	0.7417	0.7779	0.5389	0.6769	0.7807	0.7744	0.8510	0.7946	0.8069	0.8452	0.8316
	PLCC	0.7895	0.8319	0.5538	0.7720	0.8267	0.8333	0.8769	0.8464	0.8406	0.8616	0.8445
	RMSE	0.7608	0.6880	1.0322	0.7880	0.6975	0.6852	0.5959	0.6603	0.6715	0.6293	0.6639
	KROCC	0.5588	0.5977	0.3939	0.5147	0.6035	0.5951	0.6665	0.6255	0.6331	—	0.6469
CSIQ	SROCC	0.8756	0.9213	0.7671	0.9195	0.9466	0.9295	0.9310	0.9108	0.9580	0.9569	0.9198
	PLCC	0.8613	0.9144	0.8384	0.9277	0.9502	0.9179	0.9192	0.8964	0.9589	0.9586	0.9014
	RMSE	0.1334	0.1063	0.1431	0.0980	0.0818	0.1042	0.1034	0.1164	0.0745	0.0747	0.1137
	KROCC	0.6907	0.7529	0.5897	0.7537	0.7970	0.7645	0.7690	0.7374	0.8171	—	0.7487
LIVE	SROCC	0.9479	0.9567	0.9259	0.9636	0.9669	0.9401	0.9599	0.9561	0.9672	0.9660	0.9734
	PLCC	0.9449	0.9522	0.9268	0.9604	0.9675	0.9354	0.9503	0.9512	0.9651	0.9622	0.9640
	RMSE	8.9455	8.3473	10.2643	7.6137	6.9073	9.6642	7.1997	8.4327	7.1573	7.4397	8.3251
	KROCC	0.7963	0.8175	0.7579	0.8282	0.8421	0.7816	0.8366	0.8150	0.8406	—	0.8658
IVC	SROCC	0.9018	0.9125	0.8993	0.8964	0.9146	0.8192	0.9293	0.8560	0.8836	—	0.9195
	PLCC	0.9119	0.9231	0.9093	0.9028	0.9210	0.8361	0.9392	0.8662	0.8438	—	0.9298
	RMSE	0.4999	0.4686	0.5069	0.5239	0.4746	0.6684	0.4183	0.6088	0.6538	—	0.4483
	KROCC	0.7223	0.7339	0.7202	0.7158	0.7406	0.6452	0.7636	0.6609	0.6957	—	0.7488
权重平均	SROCC	0.8051	0.8376	0.6560	0.7750	0.8456	0.8306	0.8859	0.8438	0.8628	—	0.8737
	PLCC	0.8321	0.8696	0.6786	0.8353	0.8752	0.8650	0.8987	0.8730	0.8820	—	0.8772
	KROCC	0.6270	0.6662	0.5002	0.6158	0.6819	0.6575	0.7160	0.6775	0.7020	—	0.7044
直接平均	SROCC	0.8668	0.8921	0.7828	0.8641	0.9022	0.8658	0.9178	0.8794	0.9039	—	0.9111
	PLCC	0.8769	0.9054	0.8071	0.8907	0.9164	0.8807	0.9214	0.8901	0.9021	—	0.9099
	KROCC	0.6920	0.7255	0.6154	0.7031	0.7458	0.6966	0.7589	0.7097	0.7466	—	0.7526

DownLoad: CSV

表 5 IQA模型的不同失真类型SROCC值对比

Table 5. SROCC values of IQA models for different types of distortions.

数据库	失真类型	SSIM	IW-SSIM	IFC	VIF	MAD	RFSIM	FSIM_C	GSM	CVSS	MPCC	Proposed
TID2013	AGN	0.8671	0.8438	0.6612	0.8994	0.8843	0.8878	0.9101	0.9064	0.9401	0.8666	0.9359
	ANC	0.7726	0.7515	0.5352	0.8299	0.8019	0.8476	0.8537	0.8175	0.8639	0.8187	0.8653
	SCN	0.8515	0.8167	0.6601	0.8835	0.8911	0.8825	0.8900	0.9158	0.9077	0.7396	0.9276
	MN	0.7767	0.8020	0.6932	0.8450	0.7380	0.8368	0.8094	0.7293	0.7715	0.7032	0.7526
	HFN	0.8634	0.8553	0.7406	0.8972	0.8876	0.9145	0.9094	0.8869	0.9097	0.8957	0.9159
	IN	0.7503	0.7281	0.6208	0.8537	0.2769	0.9062	0.8251	0.7965	0.7457	0.6747	0.8361
	QN	0.8657	0.8468	0.6282	0.7854	0.8514	0.8968	0.8807	0.8841	0.8869	0.7931	0.8718
	GB	0.9668	0.9701	0.8907	0.9650	0.9319	0.9698	0.9551	0.9689	0.9348	0.9218	0.9614
	DEN	0.9254	0.9152	0.7779	0.8911	0.9252	0.9359	0.9330	0.9432	0.9427	0.9510	0.9466
	JPEG	0.9200	0.9187	0.8357	0.9192	0.9217	0.9398	0.9339	0.9284	0.9521	0.8964	0.9585
	JP2 K	0.9468	0.9506	0.9078	0.9516	0.9511	0.9518	0.9589	0.9602	0.9587	0.9160	0.9620
	JPTE	0.8493	0.8388	0.7425	0.8409	0.8283	0.8312	0.8610	0.8512	0.8613	0.8571	0.8644
	J2 TE	0.8828	0.8656	0.7769	0.8761	0.8788	0.9061	0.8919	0.9182	0.8851	0.8409	0.9250
	NEPN	0.7821	0.8011	0.5737	0.7720	0.8315	0.7705	0.7937	0.8130	0.8201	0.7753	0.7833
	Block	0.5720	0.3717	0.2414	0.5306	0.2812	0.0339	0.5532	0.6418	0.5152	0.5396	0.6015
	MS	0.7752	0.7833	0.5522	0.6276	0.6450	0.5547	0.7487	0.7875	0.7150	0.7520	0.7441
	CTC	0.3775	0.4593	0.1798	0.8386	0.1972	0.3989	0.4679	0.4857	0.2940	0.7814	0.4514
	CCS	0.4141	0.4196	0.4029	0.3009	0.0575	0.0204	0.8359	0.3578	0.2614	0.7054	0.3711
	MGN	0.7803	0.7728	0.6143	0.8486	0.8409	0.8464	0.8569	0.8348	0.8799	0.8766	0.8700
	CN	0.8566	0.8762	0.8160	0.8946	0.9064	0.8917	0.9135	0.9124	0.9351	0.8174	0.9168
	LCNI	0.9057	0.9037	0.8160	0.9204	0.9443	0.9010	0.9485	0.9563	0.9629	0.8095	0.9574
	ICQD	0.8542	0.8401	0.6006	0.8414	0.8745	0.8959	0.8815	0.8973	0.9108	0.8596	0.9060
	CHA	0.8775	0.8682	0.8210	0.8848	0.8310	0.8990	0.8925	0.8823	0.8523	0.8094	0.8768
	SSR	0.9461	0.9474	0.8885	0.9353	0.9567	0.9326	0.9576	0.9668	0.9605	0.9178	0.9580
CSIQ	AWGN	0.8974	0.9380	0.8431	0.9575	0.9541	0.9441	0.9359	0.9440	0.9670	0.9329	0.9652
	JPEG	0.9543	0.9662	0.9412	0.9705	0.9615	0.9502	0.9664	0.9632	0.9689	0.9564	0.9573
	JP2 K	0.9605	0.9683	0.9252	0.9672	0.9752	0.9643	0.9704	0.9648	0.9777	0.9630	0.9545
	AGPN	0.8924	0.9059	0.8261	0.9511	0.9570	0.9357	0.9370	0.9387	0.9516	0.9517	0.9492
	GB	0.9608	0.9782	0.9527	0.9745	0.9602	0.9643	0.9729	0.9589	0.9789	0.9664	0.9574
	CTC	0.7925	0.9539	0.4873	0.9345	0.9207	0.9527	0.9438	0.9354	0.9324	0.9399	0.9273
LIVE	JP2 K	0.9614	0.9649	0.9113	0.9696	0.9676	0.9323	0.9724	0.9700	0.9719	0.9608	0.9822
	JPEG	0.9764	0.9808	0.9468	0.9846	0.9764	0.9584	0.9840	0.9778	0.9836	0.9674	0.9836
	AWGN	0.9694	0.9667	0.9382	0.9858	0.9844	0.9799	0.9716	0.9774	0.9809	0.9457	0.9837
	GB	0.9517	0.9720	0.9584	0.9728	0.9465	0.9066	0.9708	0.9518	0.9662	0.9561	0.9641
	FF	0.9556	0.9442	0.9629	0.9650	0.9569	0.9237	0.9519	0.9402	0.9592	0.9627	0.9633

DownLoad: CSV

表 6 计算复杂度对比

Table 6. Time cost comparisons.

IQA模型	运行时间/s	IQA模型	运行时间/s
PSNR	0.0186	RFSIM	0.1043
SSIM	0.0892	FSIMc	0.3505
IW-SSIM	0.6424	GSM	0.1018
IFC	1.1554	CVSS	0.0558
VIF	1.1825	MPCC	—
MAD	2.7711	CAGS	0.4814

DownLoad: CSV

[1]	姚军财, 刘贵忠 2018 物理学报 67 108702 Google Scholar Yao J C, Liu G Z 2018 Acta Phys. Sin. 67 108702 Google Scholar
[2]	Athar S, Wang Z 2019 IEEE Access 7 140030 Google Scholar
[3]	Lin W S, Kuo C C J 2011 J. Vis. Commun. Image R. 22 297 Google Scholar
[4]	Chang H W, Zhang Q W, Wu Q G, Gan Y 2015 Neurocomputing 151 1142 Google Scholar
[5]	Wang Z, Bovik A C, Sheikh H R, Simoncelli E P 2004 IEEE Trans. Image Process. 13 600 Google Scholar
[6]	Sheikh H R, Bovik A C 2006 IEEE Trans. Image Process. 15 430 Google Scholar
[7]	Sheikh H R, Bovik A C, de Veciana G 2005 IEEE Trans. Image Process. 14 2117 Google Scholar
[8]	Wang Z, Simoncelli E P, Bovik A C 2003 37^th Asilomar Conference on Signals, Systems and Computer PacificGrove, CA, November 9−12, 2003 pp1398−1402
[9]	Wang Z, Li Q 2011 IEEE Trans. Image Process. 20 1185 Google Scholar
[10]	Larson E C, Chandler D M 2010 J. Electron. Imaging 19 011006 Google Scholar
[11]	Zhang L, Zhang L, Mou X Q 2010 IEEE International Conference on Image Processing Hong Kong, Peoples of China, September 26−29, 2010 pp321−324
[12]	Zhang L, Zhang L, Mou X Q, Zhang D 2011 IEEE Trans. Image Process. 20 2378 Google Scholar
[13]	Liu A M, Lin W S, Narwaria M 2012 IEEE Trans. Image Process. 21 1500 Google Scholar
[14]	Jia H Z, Zhang L, Wang T H 2018 IEEE Access 6 65885 Google Scholar
[15]	姚军财, 申静 2020 物理学报 69 148702 Google Scholar Yao J C, Shen J 2020 Acta Phys. Sin. 69 148702 Google Scholar
[16]	Robertson A R 1990 Color Res. Appl. 15 167 Google Scholar
[17]	Mahny M, Vaneycken L, Oosterlinck A 1994 Color Res. Appl. 19 105
[18]	Lee D, Plataniotis K N 2015 IEEE Trans. Image Process. 24 3950 Google Scholar
[19]	Lee D, Plataniotis K N 2014 2014 International Conference on Acoustics, Speech and Signal Processing (ICASSP) Florence, Italy, May 4−9, 2014 pp166−170
[20]	Berns R S 2014 Color Res. Appl. 39 322 Google Scholar
[21]	Zhang L, Shen Y, Li H Y 2014 IEEE Trans. Image Process. 23 4270 Google Scholar
[22]	Jain R C, Kasturi R, Schunck B G 1995 Machine Vision (New York: McGraw-Hill) pp140–185
[23]	Sonka M, Hlavac V, Boyle R 2008 Image Processing, Analysis and Machine Vision (3rd Ed.) (Stanford: Cengage Learning) p77
[24]	Xue W F, Zhang L, Mou X Q, Bovik A C 2014 IEEE Trans. Image Process. 23 684 Google Scholar
[25]	Kim D O, Han H S, Park R H 2010 IEEE Trans. Consum. Electr. 56 930 Google Scholar
[26]	Nafchi H Z, Shahkolaei A, Hedjam R, Cheriet M 2016 IEEE Access 4 5579 Google Scholar
[27]	Taguchi G, Yokoyama Y, Wu Y 1993 Taguchi Methods, Design of experiments (Dearbon, MI: ASI Press) pp59−63
[28]	Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo C C J 2015 Signal Process. Image Commun. 30 57 Google Scholar
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