基于局部标准差与显著图的模糊图像质量评价方法

doi:10.13229/j.cnki.jdxbgxb201604046

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1337-1343.doi: 10.13229/j.cnki.jdxbgxb201604046

基于局部标准差与显著图的模糊图像质量评价方法

卢彦飞^{1, 2, 3}, 张涛¹, 郑健³, 李铭^{1, 2, 3}, 章程^{1, 2, 3}

1.中国科学院长春光学精密机械与物理研究所,长春 130033;
2.中国科学院大学,北京 100049;
3.中国科学院苏州生物医学工程技术研究所,江苏苏州 215163

收稿日期:2015-02-04 出版日期:2016-07-20 发布日期:2016-07-20
通讯作者: 张涛(1964-),男,研究员,博士.研究方向:探测与成像.E-mail:zhangt@ciomp.ac.cn
作者简介:卢彦飞(1989-),男,博士研究生.研究方向:图像处理与图像质量评价.E-mail:bestluyf@126.com
基金资助:
国家自然科学基金项目(61201117,61301042); 国家重大科学仪器设备开发专项项目(2011YQ040082); 国家科技支撑计划项目(2012BA113B04)

No-reference blurring image quality assessment based on local standard deviation and saliency map

LU Yan-fei^{1, 2, 3}, ZHANG Tao¹, ZHENG Jian³, LI Ming^{1, 2, 3}, ZHANG Cheng^{1, 2, 3}

1.Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.University of Chinese Academy of Sciences, Beijing 100049, China;
3.Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China

Received:2015-02-04 Online:2016-07-20 Published:2016-07-20

摘要/Abstract

摘要： 为了有效地对模糊图像的质量进行评价,提出了基于局部标准差和显著图的无参考模糊图像质量评价方法。首先,针对待评价图像,利用高斯低通滤波器对其进行模糊化来构造参考图像。然后,利用图像的局部标准差和显著图两个特征在模糊化前后的变化情况,对原模糊图像的质量进行评价。最后分别在LIVE图像库和CSIQ图像库上对本文方法进行了验证,其中Pearson线性相关系数(PLCC)值分别达到了0.9315和0.9254,Spearman秩相关系数(SROCC)值分别达到了0.9258和0.8962。实验结果表明本文方法与当前公认性能优越的算法LPC-SI表现接近,且计算复杂度较低,耗时仅为其4.7%。

关键词: 信息处理技术, 模糊图像质量评价, 再模糊效应, 局部标准差, 显著图

Abstract: In order to evaluate the quality of blur image effectively, a no-reference image quality assessment method based on local standard deviation and saliency map is proposed. First, the Gaussian low-pass filter is used to construct a reference image through blurring the given image. Then, two features, namely local standard deviation map and saliency map, are selected to evaluate the quality of the blur image according to the changes of the two features before and after the blurring process. The proposed method is tested on LIVE database and CSIQ database, on which the Pearson linear correlation coefficients are 0.9315 and 0.9254 respectively, and the Spearman rank correlation coefficients are 0.9258 and 0.8962 respectively. Experimental results indicate that the proposed method is close to the state-of-art method LPC-SI, however its computational complexity is much lower, only about 4.7% of that of LPC-SI.

Key words: information processing, blur image quality metric, reblur effect, local standard deviation, saliency map

中图分类号:

TN911.73

卢彦飞, 张涛, 郑健, 李铭, 章程. 基于局部标准差与显著图的模糊图像质量评价方法[J]. 吉林大学学报(工学版), 2016, 46(4): 1337-1343.

LU Yan-fei, ZHANG Tao, ZHENG Jian, LI Ming, ZHANG Cheng. No-reference blurring image quality assessment based on local standard deviation and saliency map[J]. 吉林大学学报(工学版), 2016, 46(4): 1337-1343.

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基于局部标准差与显著图的模糊图像质量评价方法

No-reference blurring image quality assessment based on local standard deviation and saliency map

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