首页 > 论文 > 激光与光电子学进展 > 54卷 > 7期(pp:71001--1)

基于高阶相位一致性的混合失真图像质量评价

Multiply-Distorted Image Quality Assessment Based on High-Order Phase Congruency

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

混合失真图像质量评价是图像质量评价(IQA)领域的重点和难点,基于高阶相位一致性,提出了一种混合失真无参考IQA算法。计算了高阶相位一致性用于捕捉图像结构信息,应用灰度共生矩阵分别提取了4阶相位一致性图像的统计特征;在分析相邻阶相位一致性的相关性及相邻阶相位一致性局部熵的相关性的基础上,分别计算了相邻阶相位一致性及其局部熵的互信息和交叉熵;利用支持向量回归机制建立回归模型并进行质量预测。在MLIVE和MDID2013数据库上的实验结果表明,该算法的评价结果与主观评价分数具有很高的一致性,其性能优于当今主流的全参考和无参考IQA算法。

Abstract

Image quality assessment for multiply-distorted images is the emphasis and difficulty in image quality assessment (IQA) filed. Based on the high-order phase congruency, a no-reference IQA method for multiply-distorted images is proposed. The high-order phase congruency is computed to capture the structural information of the image. The statistical features of four orders phase congruency are extracted by gray level co-occurrence matrix,respectively. And,based on the analysis of the correlation between adjacent orders of phase congruency and the correlation between adjacent orders of local entropy of phase congruency, the mutual information and cross entropy of that are calculated. The support vector regression is utilized to build a regression model and then it is used for quality predicting. The experimental results on MLIVE and MDID2013 databases show that the proposed method has high consistency with the subjective evaluation scores and outperforms the state-of-the-art full-reference and no-reference IQA metrics.

投稿润色
补充资料

中图分类号:TN919.8

DOI:10.3788/lop54.071001

所属栏目:图像处理

基金项目:国家自然科学基金面上项目(61471262)、重点国际(地区)合作研究项目(61520106002)、教育部博士点基金(20130032110010)

收稿日期:2017-01-12

修改稿日期:2017-03-08

网络出版日期:--

作者单位    点击查看

侯春萍:天津大学电气自动化与信息工程学院, 天津 300072
马彤彤:天津大学电气自动化与信息工程学院, 天津 300072
岳广辉:天津大学电气自动化与信息工程学院, 天津 300072
冯丹丹:天津大学电气自动化与信息工程学院, 天津 300072
刘 月:天津大学电气自动化与信息工程学院, 天津 300072

联系人作者:侯春萍(hcp@tju.edu.cn)

备注:侯春萍(1957—),女,博士,教授,主要从事图像处理方面的研究。

【1】Xue Xiaobo, Yu Mei, He Meiling. Stereoscopic image-quality-assessment method based on visual cell model[J]. Laser & Optoelectronics Progress, 2016, 53(4): 041004.
薛小波, 郁 梅, 何美伶. 基于仿视觉细胞模型的立体图像质量评价方法[J]. 激光与光电子学进展, 2016, 53(4): 041004.

【2】Gu K, Zhai G, Liu M, et al. FISBLIM: a five-step blind metric for quality assessment of multiply distorted images[C]. 2013 IEEE Workshop on Signal Processing Systems (SiPS), 2013: 241-246.

【3】Gu K, Zhai G, Yang X, et al. Hybrid no-reference quality metric for singly and multiply distorted images[J]. IEEE Transactions on Broadcasting, 2014, 60(3): 555-567.

【4】Li Q, Lin W, Fang Y. No-reference quality assessment for multiply-distorted images in gradient domain[J]. IEEE Signal Processing Letters, 2016, 23(4): 541-545.

【5】Li Chengfei, Chen Xinhua. Vehicle type recognition based on combining local binary pattern and Hu matrix feature[J]. Laser & Optoelectronics Progress, 2016, 53(10): 101503.
李澄非, 陈新华. 融合局部二值模式和Hu矩特征的车型识别[J]. 激光与光电子学进展, 2016, 53(10): 101503.

【6】Ghosh K, Sarkar S, Bhaumik K. Understanding image structure from a new multi-scale representation of higher order derivative filters[J]. Image and Vision Computing, 2007, 25(8): 1228-1238.

【7】Yuan Weiqi, Fan Yonggang, Ke Li. Palmprints recognition method based on the phase consistency combined with log-gabor filter[J]. Acta Optica Sinica, 2010, 30(1): 147-152.
苑玮琦, 范永刚, 柯 丽. 相位一致性和对数Gabor滤波器相结合的掌纹识别方法[J]. 光学学报, 2010, 30(1): 147-152.

【8】Kovesi P. Phase congruency detects corners and edges[C]. The Australian Pattern Recognition Society Conference: DICTA, 2003: 309-318.

【9】Gu K, Zhai G, Yang X, et al. A new reduced-reference image quality assessment using structural degradation model[C]. 2013 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, 2013: 1095-1098.

【10】Jayaraman D, Mittal A, Moorthy A K, et al. Objective quality assessment of multiply distorted images[C]. Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012, 43(4): 1693-1697.

【11】Haralick R M, Shanmugam K. Textural features for image classification[J]. IEEE Transactions on Systems, Man & Cybernetics, 1973, 3(6): 610-621.

【12】Li Meng, Hua Weiping, Zhao Jufeng. Dual-band image fusion using visual attention extraction with multiple windows[J]. Laser & Optoelectronics Progress, 2015, 52(12): 121002.
李 梦, 华玮平, 赵巨峰. 使用多尺度视觉注意提取的双波段图像融合[J]. 激光与光电子学进展, 2015, 52(12): 121002.

【13】Zhao Shusen, Chen Sijia, Shen Jingling. Identification of terahertz absorption spectra of illicit drugs using support vector machines[J]. Chinese J Lasers, 2009, 36(3): 752-757.
赵树森, 陈思嘉, 沈京玲. 用支持向量机识别毒品的太赫兹吸收光谱[J]. 中国激光, 2009, 36(3): 752-757.

【14】Chen Jing, Jiang Hao, Liu Tundong, et al. Optimization for raman fiber amplifiers based on least squares support vector regression model[J]. Acta Optica Sinica, 2015, 35(11): 1123004.
陈 静, 江 灏, 刘暾东, 等. 基于最小二乘支持向量回归模型的拉曼光纤放大器优化设计[J]. 光学学报, 2015, 35(11): 1123004.

【15】Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004,13(4): 600-612.

【16】Larson E C, Chandler D M. Most apparent distortion: full-reference image quality assessment and the role of strategy[J]. Journal of Electronic Imaging, 2010, 19(1): 011006.

【17】Zhang L, Zhang L, Mou X, et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.

【18】Xue W, Zhang L, Mou X, et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 2014, 23(2): 684-695.

【19】Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708.

【20】Zhang L, Zhang L, Bovik A C. A feature-enriched completely blind image quality evaluator[J]. IEEE Transactions on Image Processing, 2015, 24(8): 2579-2591.

【21】Xue W, Mou X, Zhang L, et al. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4850-4862.

引用该论文

Hou Chunping,Ma Tongtong,Yue Guanghui,Feng Dandan,Liu Yue. Multiply-Distorted Image Quality Assessment Based on High-Order Phase Congruency[J]. Laser & Optoelectronics Progress, 2017, 54(7): 071001

侯春萍,马彤彤,岳广辉,冯丹丹,刘 月. 基于高阶相位一致性的混合失真图像质量评价[J]. 激光与光电子学进展, 2017, 54(7): 071001

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF