融合图像质量评价指标的相关性分析及性能评估

张小利; 李雄飞; 李军

doi:10.3724/SP.J.1004.2014.00306

融合图像质量评价指标的相关性分析及性能评估

doi: 10.3724/SP.J.1004.2014.00306

张小利^1,2,
李雄飞^1,2,
李军³

1.
吉林大学计算机科学与技术学院长春 130012;
2.
吉林大学符号计算与知识工程教育部重点实验室长春 130012;
3.
长春理工大学应用数学系长春 130022

基金项目:

吉林省科技发展计划（20090468，20100508，201105017）;长春市科技计划（11KZ24）资助

详细信息

作者简介:
张小利吉林大学计算机科学与技术学院博士研究生. 主要研究方向为图像处理，信息融合.E-mail：zxl12@mails.jlu.edu.cn

计量
- 文章访问数: 3141
- HTML全文浏览量: 174
- PDF下载量: 2130
- 被引次数: 0
出版历程
- 收稿日期: 2012-09-12
- 修回日期: 2013-01-28
- 刊出日期: 2014-02-20

Validation and Correlation Analysis of Metrics for Evaluating Performance of Image Fusion

1.
College of Computer Science and Technology, Jilin University, Changchun 130012;
2.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012;
3.
Department of Applied Mathematics, Changchun University of Science and Technology, Changchun 130022

Funds:

Supported by Technology Development Plan of Jilin Province (20090468, 20100508, 201105017) and Science and Technology Project of Changchun (11KZ24)

摘要

摘要: 图像融合质量评价指标研究旨在提供一种高效、准确的方法，为融合模型选择、参数优化等问题提供支持. 本文通过对现有指标的机理分析、指标性能检验与指标间相关性分析，提出一种客观评价指标集的遴选策略. 本文首先将现有客观评价指标归为三类：基于统计的、基于信息的和基于人类视觉系统的；之后列举了类别内经典指标和最新指标；并在标准数据集上，使用正确排序指标对各图像融合客观评价指标的性能进行验证. 结果表明，基于视觉系统类的指标性能普遍优于前两类. 最后，利用Spearman相关系数挖掘各指标间的相关程度. 实验表明，通过指标性能和相关系数可以选取合适的客观评价指标集.
- 图像融合 /
- 客观指标 /
- 性能分析 /
- 相关性分析
Abstract: Image fusion performance evaluation aims at providing an efficient and accurate method for the fusion model choosing, parameter optimizing and the like. By analyzing the mechanism of existing metrics in theory and testing the performance of metrics and correlations with each other experimentally, the paper presents an effective metric set selection strategy. First of all, existing metrics are classified into three categories: statistics-based, information-based and human-visual-system based classes; secondly, we enumerate the classical or the latest metrics for each class. In addition, we test the performance of objective evaluating metrics in terms of correct ranking by running on a standard data set, and the results indicate that human-visual-system based metrics are superior to others. Finally, we explore correlations among metrics using Spearman correlation coefficient. Experimental results indicate that we can choose a proper objective evaluating metric set by means of performances and correlations of metrics.
- Image fusion /
- objective metrics /
- performance analysis /
- correlation analysis

HTML全文

参考文献(26)

[1]	Goshtasby A A, Nikolov S. Image fusion: advances in the state of the art. Information fusion, 2007, 8(2): 114-118
[2]	Yang B, Li S T. Pixel-level image fusion with simultaneous orthogonal matching pursuit. Information fusion, 2012, 13(1): 10-19
[3]	Hu Liang-Mei, Gao Jun, He Ke-Feng. Research on quality measures for image fusion. Acta Electronica Sinica, 2004, 32(12A): 218-221(胡良梅, 高隽, 何柯峰. 图像融合质量评价方法的研究. 电子学报, 2004, 32(12A): 218-221)
[4]	Petrović V. Subjective tests for image fusion evaluation and objective metric validation. Information Fusion, 2007, 8(2): 208-216
[5]	Toet A, Franken E M. Perceptual evaluation of different image fusion schemes. Displays, 2003, 24(1): 25-37
[6]	Qu G H, Zhang D L, Yan P F. Information measure for performance of image fusion. Electronics Letters, 2002, 38(7): 313-315
[7]	Petrović V, Cootes T. Information representation for image fusion evaluation. In: Proceedings of the 9th International Conference on Information Fusion. Florence, Italy: IEEE, 2006. 1-7
[8]	Hossny M, Nahavandi S, Creighton D. Comments on “Information measure for performance of image fusion”. Electronics Letters, 2008, 44(18): 1066-1067
[9]	Xydeas C S, Petrović V. Objective image fusion performance measure. Electronics Letters, 2000, 36(4): 308-309
[10]	Wang Z, Bovik A C. A universal image quality index. IEEE Signal Processing Letters, 2002, 9(3): 81-84
[11]	Wang Z, Simoncelli E P, Bovik A C. Multiscale structural similarity for image quality assessment. In: Conference Record of the 37th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA: IEEE, 2003. 1398-1402
[12]	Sampat M P, Wang Z, Gupta S, Bovik A C, Markey M K. Complex wavelet structural similarity: a new image similarity index. IEEE Transactions on Image Processing, 2009, 18(11): 2385-2401
[13]	Piella G, Heijmans H. A new quality metric for image fusion. In: Proceedings of the 2003 IEEE International Conference on Acoustics, Speech and Signal Processing. Barcelona, Spain: IEEE, 2003. 173-176
[14]	Yang C, Zhang J Q, Wang X R, Liu X. A novel similarity based quality metric for image fusion. Information Fusion, 2008, 9(2): 156-160
[15]	Luo X Y, Zhang J, Dai Q H. Saliency-based geometry measurement for image fusion performance. IEEE Transactions on Instrumentation and Measurement, 2012, 61(4): 1130-1132
[16]	Zheng Y Z, Qin Z. Objective image fusion quality evaluation using structural similarity. Tsinghua Science and Technology, 2009, 14(6): 703-709
[17]	Zhang X Q. A novel quality metric for image fusion based on color and structural similarity. In: Proceedings of the 2009 International Conference on Signal Processing Systems. Singapore, Singapore: IEEE, 2009. 258-262
[18]	Chen H, Varshney P K. A human perception inspired quality metric for image fusion based on regional information. Information Fusion, 2007, 8(2): 193-207
[19]	Han Y, Cai Y Z, Cao Y, Xu X M. A new image fusion performance metric based on visual information fidelity. Information Fusion, 2013, 14(2): 127-135
[20]	Wang Z, Bovik A C, Lu L G. Why is image quality assessment so difficult? In: Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. Orlando, FL, USA: IEEE, 2002. 3313-3316
[21]	Wang Z, Bovik A C. Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine, 2009, 26(1): 98-117
[22]	Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600-612
[23]	Cvejic N, Canagarajah C N, Bull D R. Image fusion metric based on mutual information and Tsallis entropy. Electronics Letters, 2006, 42(11): 626-627
[24]	Wang Z, Bovik A C. A universal image quality index. Signal Processing Letters, 2002, 9(3): 81-84
[25]	Wang Z, Bovik A C. Modern image quality assessment. Synthesis Lectures on Image, Video, and Multimedia Processing. USA: Morgan Claypool Publishers, 2006, 2(1): 1-156
[26]	Cheng Guang-Quan, Zhang Ji-Dong, Cheng Li-Zhi, Huang Jin-Cai, Liu Zhong. Image quality assessment based on geometric structural distortion model. Acta Automatica Sinica, 2011, 37(7): 811-819(程光权, 张继东, 成礼智, 黄金才, 刘忠. 基于几何结构失真模型的图像质量评价研究. 自动化学报, 2011, 37(7): 811-819)