Color Image Quality Assessment Based on Quaternion Spectral Residual

Jing Yue; Guojun Liu; Hao Fu

doi:10.3788/LOP56.031009

Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 3 >Page 031009 > Article

Laser & Optoelectronics Progress
Vol. 56, Issue 3, 031009 (2019)

Color Image Quality Assessment Based on Quaternion Spectral Residual

Jing Yue^**, Guojun Liu^*, and Hao Fu

Author Affiliations

School of Mathematics & Statistics, Ningxia University, Yinchuan, Ningxia 750021, China

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DOI: 10.3788/LOP56.031009 Cite this Article Set citation alerts

Jing Yue, Guojun Liu, Hao Fu. Color Image Quality Assessment Based on Quaternion Spectral Residual[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031009 Copy Citation Text

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Fig. 1. Schematic of color imageconvert to a pure quaternion matrix

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Quaternion gradient maps of color images. (a) Color images; (b) corresponding quaternion gradient maps of (a); (c) color images; (d) corresponding quaternion gradient maps of (c)

Fig. 2. Quaternion gradient maps of color images. (a) Color images; (b) corresponding quaternion gradient maps of (a); (c) color images; (d) corresponding quaternion gradient maps of (c)

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Fig. 3. Flowchart of QSR-SIM algorithm

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Fig. 4. Regression curves of different image quality evaluation algorithms in TID2013. (a) SSIM; (b) FISM; (c) MS-GMSDc; (d) QSSIM; (e) SR-SIM; (f) QSR-SIM

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Fig. 5. Reference image and its distortion images of different types. (a) Reference image I07; (b) I07_11_5; (c) I07_12_4; (d) I07_15_3; (e) I07_17_4; (f) I07_24_5

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Algorithm	I07_11_5	I07_12_4	I07_15_3	I07_17_4	I07_24_5
Subjective score	0.9773	2.8636	3.0455	6.7273	0.4762
QSR-SIM	0.9701	0.9919	0.9968	0.9976	0.9691
VSI^[4]	0.8964	0.9610	0.9614	0.9868	0.8875
SR-SIM^[3]	0.8283	0.9317	0.9636	0.9803	0.8056
FSIM^[5]	0.7203	0.9150	0.9610	0.9621	0.7137
SSIM^[21]	0.5922	0.7208	0.9683	0.9516	0.6019
VSNR^[23]	8.7561	15.0237	12.223	11.3239	8.6929
PSNR^[24]	23.0007	26.1743	27.2014	25.0836	23.0449
GMSD^[25]	0.2418	0.1215	0.1291	0.0259	0.2622

Table 1. Results of five kinds of distorted pictures of reference image I07 in different evaluation algorithm experiments

Algorithm	I07_11_5	I07_12_4	I07_15_3	I07_17_4	I07_24_5
Subjective score	4	3	2	1	5
QSR-SIM	4	3	2	1	5
VSI	4	3	2	1	5
SR-SIM	4	3	2	1	5
FSIM	4	3	2	1	5
SSIM	5	3	1	2	4
VSNR	4	1	2	3	5
PSNR	5	2	1	3	4
GMSD	4	2	3	1	5

Table 2. Ranking of different evaluation algorithms

Database	Time /s
Database	QSSIM	QSR-SIM
I17_11_2	1.1124	0.7474
TID2013	520	485

Table 3. Comparison of time complexity of algorithms

Database	Parameter	PSNR	SSIM	FSIM	GMSD	MS-GMSD	SR-SIM	QSR-SIM
TID2013	SROCC	0.6869	0.7417	0.8015	0.8030	0.8139	0.8073	0.8169
	KROCC	0.4958	0.5588	0.6289	0.6352	0.6467	0.6404	0.6268
	PLCC	0.6748	0.7895	0.8589	0.8575	0.8618	0.7970	0.8413
	RMSE	0.9149	0.7608	0.6349	0.4762	0.6288	0.6193	0.6701
CSIQ	SROCC	0.8058	0.8756	0.9242	0.9515	0.9545	0.9319	0.9435
	KROCC	0.6084	0.6907	0.7567	0.8021	0.8075	0.7725	0.7821
	PLCC	0.8000	0.8613	0.9120	0.9453	0.9512	0.9250	0.8049
	RMSE	0.1575	0.1334	0.1077	0.0856	0.0810	0.0997	0.1618

Table 4. Comparison of various color image quality assessment methods' performance

Database	Distortiontype	MS-SSIM^[26]	SSIM_I^[27]	SSIM	IFC^[28]	VSNR	IW-SSIM	FSIM	SR-SIM	VSI	QSR-SIM
TID2013	AGN	0.8646	0.8627	0.8671	0.6612	0.8271	0.8438	0.8973	0.9253	0.9460	0.9138
	ANC	0.7730	0.7763	0.7726	0.5352	0.7305	0.7515	0.8208	0.8570	0.8705	0.8111
	SCN	0.8544	0.8505	0.8515	0.6601	0.8013	0.8167	0.8750	0.9225	0.9367	0.8900
	MN	0.8073	0.7895	0.7767	0.6932	0.7072	0.8020	0.7944	0.7860	0.7697	0.7036
	HFN	0.8604	0.8688	0.8634	0.7406	0.8455	0.8553	0.8984	0.9132	0.9200	0.9042
	IN	0.7629	0.7896	0.7503	0.6408	0.7363	0.7281	0.8072	0.8277	0.8741	0.7885
	QN	0.8706	0.8411	0.8657	0.6282	0.8357	0.8468	0.8719	0.8502	0.8748	0.8626
	GB	0.9673	0.9724	0.9668	0.8907	0.9470	0.9701	0.9551	0.9620	0.9612	0.9118
	DEN	0.9268	0.9296	0.9254	0.7779	0.9081	0.9152	0.9302	0.9403	0.9484	0.9366
	JPEG	0.9265	0.9227	0.9200	0.8357	0.9008	0.9187	0.9324	0.9386	0.9541	0.9219
	JP2K	0.9504	0.9575	0.9468	0.9078	0.9273	0.9506	0.9577	0.9674	0.9706	0.9522
	JGTE	0.8475	0.8581	0.8493	0.7425	0.7908	0.8388	0.8464	0.8543	0.9216	0.7764
	J2TE	0.8889	0.8856	0.8828	0.7769	0.8407	0.8656	0.8913	0.9166	0.9228	0.8355
	NEPN	0.7968	0.7885	0.7821	0.5737	0.6653	0.8011	0.7917	0.7975	0.8060	0.8141
	Block	0.4801	0.4563	0.5720	0.2414	0.1771	0.3717	0.5489	0.4731	0.1713	0.5545
	MS	0.7906	0.7845	0.7752	0.5522	0.4871	0.7833	0.7531	0.6576	0.7700	0.7762
	CTC	0.4634	0.3800	0.3775	0.1798	0.3320	0.4593	0.4686	0.4705	0.4754	0.4671
	CCS	0.4099	0.4208	0.4141	0.4029	0.3677	0.4196	0.2748	0.2053	0.8100	0.5359
	MGN	0.7786	0.8092	0.7803	0.6143	0.7644	0.7728	0.8469	0.8778	0.9117	0.8110
	CN	0.8528	0.8711	0.8566	0.8160	0.8683	0.8762	0.9121	0.9263	0.9243	0.9084
	LCNI	0.9068	0.9173	0.9057	0.8180	0.8821	0.9037	0.9466	0.9608	0.9564	0.9406
	ICQD	0.8555	0.8351	0.8542	0.6006	0.8667	0.8401	0.8760	0.8803	0.8839	0.8795
	CHA	0.8784	0.8771	0.8775	0.8210	0.8645	0.8682	0.8715	0.8754	0.8906	0.8494
	SSR	0.9483	0.9488	0.9461	0.8885	0.9339	0.9474	0.9565	0.9614	0.9628	0.9625

Table 5. SROCC values for different distortion types for various evaluation algorithms in the TID2013 database

Jing Yue, Guojun Liu, Hao Fu. Color Image Quality Assessment Based on Quaternion Spectral Residual[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031009

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