Color image quality assessment based on noise model of human vision perception and color image quality optimization

Xie Zheng-Xiang; Wang Zhi-Fang; Xiong Xing-Liang; Hu Qin

doi:10.11834/jig.20101015

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Color image quality assessment based on noise model of human vision perception and color image quality optimization
Vol. 15, Issue 10, Pages: 1454(2010)
Published Online：27 October 2010，

Published：2010
DOI： 10.11834/jig.20101015
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DOI：

Xie Zheng-Xiang, Wang Zhi-Fang, Xiong Xing-Liang, Hu Qin. Color image quality assessment based on noise model of human vision perception and color image quality optimization[J]. Journal of Image and Graphics, 2010, 15(10): 1454. DOI： 10.11834/jig.20101015.

摘要

建立一种基于人类视觉感知噪声模型的彩色图像质量评价方法用以评价含噪声图像的彩色图像质量。该方法用视觉感知图像质量的质量四参数：信息熵、平均对比度、平均灰度和关键区域图像的标准差，通过实验建立综合的彩色图像质量评价模型，称为最好质量彩色图像评价函数。该函数的最大值对应最好质量彩色图像。该方法的评价结果更符合视觉感知效果。该方法基于图像质量四参数的独立计算，不但不借助于任何参考图像，还能用于评价参考图像质量。如果参考图像质量不够好，还能通过称为Zadeh-X变换的灰度变换获得最好质量的彩色图像。

Abstract

The object of this study is to establish a universal color image quality assessment method based on human vision function characteristics for perceiving noise in an image in order to assess color image quality including noisy color image. A comprehensive image quality assessment model called as the best quality assessment function for color images is established by means of four quality parameters of image quality，information entropy

averaging contrast

averaging gray and the standard deviation in a key region of an image. The maximum of the function corresponds to the color image with the best quality. The results assessed by this method agree with human vision perception. The method

which founds on auto-computation for four parameters characterizing image quality

is not only in no need any reference image but also can be used to assess the reference image quality. The method can be also used to acquire the color image with the best quality by means of a reference image through a gray transformation our called Zadeh-X transformation if the reference image quality is not good enough.