基于差异激励的无参考图像质量评价

陈勇; 吴明明; 房昊; 刘焕淋

doi:10.16383/j.aas.c180088

基于差异激励的无参考图像质量评价

doi: 10.16383/j.aas.c180088

陈勇^1,,
吴明明^1,,
房昊^1,,
刘焕淋^2, ,

1.
重庆邮电大学工业物联网与网络化控制教育部重点实验室重庆 400065
2.
重庆邮电大学通信与信息工程学院重庆 400065

基金项目:

国家自然科学基金 60975008

重庆市研究生科研创新项目 CYS17235

详细信息

作者简介:
陈勇  重庆邮电大学自动化学院教授. 2003年获得重庆大学机械工程专业博士.主要研究方向为图像处理与模式识别, 智能优化控制. E-mail: chenyong@cqupt.edu.cn

吴明明  重庆邮电大学自动化学院硕士研究生.主要研究方向为图像处理, 机器学习与计算机视觉. E-mail:darcy.wu@unisoc.com

房昊   重庆邮电大学自动化学院硕士研究生.主要研究方向为图像处理, 机器学习与计算机视觉. E-mail: f1010507348@163.com

通讯作者:
刘焕淋重庆邮电大学通信与信息工程学院教授. 2008年获得重庆大学光电工程专业博士.主要研究方向为信息获取与处理, 机器学习与计算机视觉.本文通信作者. E-mail: liuhl@cqupt.edu.cn

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出版历程
- 收稿日期: 2018-02-06
- 录用日期: 2018-08-28
- 刊出日期: 2020-08-26

No-reference Image Quality Assessment Based on Differential Excitation

CHEN Yong^1
,,
WU Ming-Ming^1
,,
FANG Hao^1
,,
LIU Huan-Lin^{2
, ,}

1.
Key Laboratory of Industrial Internet of Thing and Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065
2.
School of Telecommunications and Information, Chongqing University of Posts and Telecommunications, Chongqing 400065

Funds:

National Natural Science Foundation of China 60975008

Chongqing Graduate Student Science Research Innovation of China CYS17235

More Information

Author Bio:
CHEN Yong    Professor at the School of Automation, Chongqing University of Posts and Telecommunication. He received his Ph. D. degree in mechanical engineering from Chongqing University in 2003. His research interest covers image processing, pattern recognition, and intelligent optimizing controls

WU Ming-Ming   Master student at the School of Automation, Chongqing University of Posts and Telecommunication. His research interest covers image processing, machine learning, and computer vision

FANG Hao    Master student at the School of Automation, Chongqing University of Posts and Telecommunication. His research interest covers image processing, machine learning, and computer vision

Corresponding author: LIU Huan-Lin Professor at the School of Telecommunication and Information Engineering, Chongqing University of Posts and Telecommunication. She received her Ph. D. degree in optical engineering from Chongqing University in 2008. Her research interest covers information acquiring and processing, machine learning, and computer vision. Corresponding author of this paper

摘要

摘要: 为了衡量图像的降质程度, 充分考虑像素间的相关性, 提出了一种基于差异激励的无参考图像质量评价算法.该算法根据韦伯定律求得差异激励图, 并依据各向异性得到差异激励的梯度映射图; 然后量化差异激励得到差异量化图, 并分别与差异激励图与梯度映射图进行加权融合; 最后利用求得的特征, 通过支持向量回归(Support vector regression, SVR)预测得出图像质量的客观评价值.在LIVE、MLIVE、MDID2013和MDID2016等多个数据库中测试显示, 该算法稳定性强, 复杂度低, 能准确反映人类对图像质量的视觉感知效果.
- 图像质量评价 /
- 韦伯定律 /
- 差异激励 /
- 梯度映射
Abstract: In order to estimate the degradation of the image distortion level and consider the correlation among pixels, a no-reference image quality assessment algorithm based on differential excitation is proposed in this article. According to the Weber$'$s law, the differential excitation map was obtained and the gradient map of differential excitation was obtained by anisotropy. Then, the differential quantization map was obtained by quantifying differential excitation, and weighted fusion with differential excitation map and gradient map is carried out respectively. Finally, the objective evaluation value of image quality is obtained by support vector regression (SVR) prediction using the acquired features. In LIVE and MLIVE and MDID2013 and MDID2016 databases, the experiment shows that the algorithm is highly robust and low complexity, which can accurately reflect the human image quality of visual perception.
- Image quality assessment /
- Weber's law /
- differential incentives /
- gradient mapping
Recommended by Associate Editor LIU Yue-Hu
注释:

1) 本文责任编委刘跃虎

HTML全文

图 1 不同程度失真图像对应的差异激励图和梯形映射图

Fig. 1 Difference excitation diagram and trapezoid map corresponding to different degree distortion images

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图 2 定向的梯度滤波器

Fig. 2 Directional gradient filter

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图 3 差异量化图与局部量化值

Fig. 3 Difference quantization map and local quantization value

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图 4 差异量化图的概率统计直方图

Fig. 4 Probability statistical histogram of difference quantization map

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图 5 本文算法流程框图

Fig. 5 The algorithm flow chart in this paper

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表 1 本文选用的6个图像库描述

Table 1 The descriptions of six image databases selected in this paper

图像库	参考图像	失真类型		图像个数
LIVE	29	JPEG2000压缩		953
		JPEG压缩
		高斯白噪声
		高斯模糊
		快衰弱
CSIQ	30	加性高斯噪声		900
		高斯模糊
		对比度改变
		粉红噪声
MLIVE	15	JPEG压缩		450
		JPEG2000压缩
		模糊+压缩
		模糊+噪声
MDID2013	12	模糊+压缩+噪声		324
MDID2016	20	噪声+模糊+对比度+压缩+ JP2K压缩		1 600
TID2013	25	#1加性高斯噪声	#13 JPEG2000传输误差	3 000
		#2彩色分量中的差分加性噪声	#14无偏心率类型噪声
		#3空域相关噪声	#15不同强度局部块失真
		#4掩膜噪声	#16均值平移
		#5高频噪声	#17对比度改变
		#6脉冲噪声	#18色彩饱和度改变
		#7量化噪声	#19乘性高斯噪声
		#8高斯模糊	#20舒适噪声
		#9图像去噪	#21噪声图像的有损压缩
		#10 JPEG压缩	#22图像的颜色量化及波动
		#11 JPEG2000压缩	#23图像色差
		#12 JPEG传输误差	#24稀疏采样及重构

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表 2 LIVE和CSIQ数据库中单一型算法质量评价性能对比

Table 2 Comparison of performance evaluation of single algorithm in LIVE and CSIQ databases

	LIVE (953 images)			CSIQ (900 images)
算法	CC	SROCC	RMSE	CC	SROCC	RMSE
DIIVINE^[5]	0.893	0.885	11.168	0.797	0.810	0.275
BRISQUE^[6]	0.944	0.947	7.795	0.728	0.740	0.325
NIQE^[7]	0.909	0.908	11.376	0.756	0.739	0.340
IL-NIQE^[8]	0.906	0.903	10.824	0.732	0.718	0.354
NR-GLBP^[9]	0.942	0.935	9.075	0.847	0.801	0.174
NRSL^[10]	0.957	0.953	8.018	0.859	0.851	0.109
本文算法	0.963	0.961	7.052	0.858	0.839	0.117

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表 3 TID2013数据库中算法质量评价性能指标SROCC对比(3 000幅图)

Table 3 Comparison of quality evaluation performance indexes of algorithm in TID2013 database (3 000 images)

算法	#1	#2	#3	#4	#5	#6	#7	#8	#9	#10	#11	#12	#13	#14	#15	#16	#17	#18	#19	#20	#21	#22	#23	#24	All
BRISQUE^[6]	0.706	0.523	0.776	0.295	0.836	0.802	0.682	0.861	0.500	0.790	0.779	0.254	0.723	0.213	0.197	0.217	0.079	0.113	0.674	0.198	0.627	0.849	0.724	0.811	0.567
NR-GLBP^[9]	0.466	0.591	0.759	0.491	0.875	0.693	0.833	0.878	0.721	0.844	0.867	0.440	0.594	0.226	0.204	0.105	0.123	0.023	0.580	0.447	0.507	0.762	0.748	0.830	0.679
NRSL^[10]	0.813	0.457	0.867	0.393	0.902	0.787	0.700	0.886	0.795	0.818	0.891	0.345	0.805	0.117	0.323	0.136	0.194	0.110	0.753	0.434	0.751	0.866	0.694	0.887	0.661
NFERM^[13]	0.851	0.520	0.846	0.521	0.894	0.857	0.785	0.888	0.741	0.797	0.920	0.381	0.718	0.176	0.081	0.238	0.056	0.029	0.762	0.206	0.401	0.848	0.684	0.878	0.652
GWH-GLBP^[14]	0.736	0.358	0.814	0.412	0.874	0.795	0.757	0.838	0.811	0.890	0.901	0.494	0.656	0.326	0.344	0.341	0.252	0.420	0.601	0.624	0.664	0.741	0.919	0.898	0.655
本文算法	0.768	0.454	0.861	0.537	0.885	0.814	0.752	0.908	0.859	0.853	0.940	0.544	0.754	0.426	0.480	0.275	0.442	0.507	0.706	0.680	0.823	0.839	0.948	0.903	0.691

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表 4 MLIVE、MDID2013和MDID2016数据库中混合型算法质量评价性能指标对比

Table 4 Comparison of quality evaluation performance indicators of hybrid algorithm in MLIVE, MDID2013 and MDID2016 databases

算法	MLIVE			MDID2013			MDID2016
	(450 images)			(324 images)			(1 600 images)
	CC	SROCC	RMSE	CC	SROCC	RMSE	CC	SROCC	RMSE
SISBLM^[10]	0.925	0.907	7.198	0.910	0.905	0.019	0.633	0.655	1.708
HOSA^[12]	0.926	0.902	6.974	0.892	0.872	0.021	0.566	0.551	1.871
NFERM^[13]	0.919	0.899	7.458	0.871	0.855	0.025	0.496	0.451	1.915
GWH-GLBP^[14]	0.945	0.939	6.061	0.913	0.907	0.019	0.891	0.886	1.004
本文算法	0.957	0.942	5.736	0.916	0.904	0.019	0.903	0.892	0.947

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表 5 测试不同训练与测试比例的SROCC和CC的中值(1 000次)

Table 5 Median values of SROCC and CC for different training and test ratios (1 000 times)

		LIVE						MLIVE
测试集和训练集比例	指标	JP2K	JPEG	GBLUR	FF	WN	ALL	GB + JPEG	GB + WN	ALL
70 $\%$与30 $\%$	SROCC	0.9496	0.9592	0.9419	0.8848	0.9752	0.9604	0.9372	0.9438	0.9366
	CC	0.9613	0.9768	0.9504	0.8958	0.9804	0.9624	0.9598	0.9535	0.9506
60 $\%$与40 $\%$	SROCC	0.9479	0.9570	0.9397	0.8732	0.9747	0.9578	0.9266	0.9321	0.9278
	CC	0.9594	0.9750	0.9468	0.8875	0.9796	0.9610	0.9483	0.9401	0.9414
50 $\%$与50 $\%$	SROCC	0.9466	0.9546	0.9363	0.8674	0.9743	0.9559	0.9204	0.9240	0.9223
	CC	0.9587	0.9738	0.9413	0.8839	0.9791	0.9595	0.9437	0.9357	0.9364
40 $\%$与60 $\%$	SROCC	0.9409	0.9521	0.9320	0.8606	0.9706	0.9522	0.9063	0.9053	0.9046
	CC	0.9520	0.9691	0.9369	0.8744	0.9789	0.9568	0.9243	0.9167	0.9164
30 $\%$与70 $\%$	SROCC	0.9351	0.9465	0.9253	0.8514	0.9645	0.9463	0.9040	0.8984	0.8976
	CC	0.9457	0.9651	0.9287	0.8611	0.9781	0.9507	0.9206	0.9101	0.9112
20 $\%$与80 $\%$	SROCC	0.9253	0.9345	0.9138	0.8329	0.9612	0.9345	0.8864	0.8785	0.8811
	CC	0.9351	0.9540	0.9150	0.8468	0.9748	0.9387	0.9079	0.8862	0.8918

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表 6 CSIQ数据库中不同失真类型的性能评价

Table 6 Performance evaluation of different distortion types in CSIQ database

类型	CC	SROCC	RMSE	KROCC
JP2K	0.9046	0.8741	7.6044	0.6966
JPEG	0.9360	0.9194	6.5040	0.8151
GBLUR	0.8858	0.9016	7.8402	0.7320
WN	0.9377	0.9238	6.3493	0.7591
ALL	0.9167	0.8953	7.2687	0.7378

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表 7 LIVE数据库中失真类型的识别准确率(1 000次)

Table 7 Recognition accuracy of distortion type in LIVE database (1 000 times)

类型	JP2K	JPEG	GBLUR	FF	WN	ALL
准确率	87.94 $\%$	100 $\%$	97.82 $\%$	90.26 $\%$	100 $\%$	95.39 $\%$

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表 8 图像质量评价算法运行时间

Table 8 Running time of image quality evaluation algorithm

IQA model	DIIVINE	BRISQUE	NIQA	SISBLM	HOSA	NFERM	GWH-GLBP	本文算法
Time (s)	0.18	15.8	2.72	3.73	0.35	55.1	0.27	0.33

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