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应用科学学报 ›› 2019, Vol. 37 ›› Issue (3): 398-406.doi: 10.3969/j.issn.0255-8297.2019.03.010

• 信号与信息处理 • 上一篇    下一篇

一种彩色图像质量评价方法

曹欣1, 李战明1, 胡文瑾2   

  1. 1. 兰州理工大学 电气工程与信息工程学院, 兰州 730050;
    2. 西北民族大学 数学与计算机科学学院, 兰州 730000
  • 收稿日期:2018-08-02 修回日期:2018-10-22 出版日期:2019-05-31 发布日期:2019-05-31
  • 通信作者: 李战明,教授,博导,研究方向:复杂系统的建模与控制、智能信息处理、模式识别,E-mail:liuzm@lut.edu.cn E-mail:liuzm@lut.edu.cn
  • 基金资助:
    国家自然科学基金（No.61561042）；西北民族大学"一优三特"学科中央高校基本科研业务费基金（No.31920180117）资助

A Method for Color Image Quality Assessment

CAO Xin1, LI Zhanming1, HU Wenjin2   

  1. 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
    2. College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730000, China
  • Received:2018-08-02 Revised:2018-10-22 Online:2019-05-31 Published:2019-05-31

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摘要： 人眼视觉系统对图像的结构信息高度敏感，且与色彩信息密切相关.但是基于结构相似性图像质量评价方法大多是在不考虑颜色的情况下实现的，为此提出了一种新的图像质量评价方法.首先根据人眼视觉系统特性提取彩色图像的亮度、色调和饱和度，将亮度分量与Scharr算子进行卷积，提取图像亮度通道的边缘特征得到亮度变化强烈部分的边缘特征，同时将色调和饱和度作为色彩特征进行处理；其次提取图像灰度化后的边缘特征以得到亮度变化缓慢部分的边缘特征；最后融合以上特征建立彩色图像质量评价模型.在LIVE数据库上进行的对比实验表明：和其他被广泛采用的图像质量评价算法相比，该算法评价结果总体上与主观评价结果具有更高的一致性.

关键词: 图像质量评价, 色彩特征, 边缘特征, 质量融合, HSV色彩空间

Abstract: Human visual system is not only highly sensitive to the structural information of the image, but also closely related to the color information. Image quality assessment methods based on structural similarity are mostly implemented without considering color. Aiming at this problem, a new image quality assessment method is proposed. The proposed method first extracts the value, hue and saturation of the color image according to the characteristics of the human visual system, and convolves the value component with the Scharr operator to extract the image value channel edge feature to obtain the edge feature of the intense-changing part of brightness, and simultaneously the hue and saturation are treated as the color feature. Secondly, the method extracts the edge feature of the grayedout image to obtain the edge feature of the slow-changing part of the brightness, and finally fuses the above features to obtain more complete image features so as to establish a color image quality assessment model. A large number of comparative experiments were performed on the LIVE database. The results show that the assessment results of the algorithm are generally more consistent with the subjective assessment results, compared with other widely used image quality assessment algorithms.

Key words: HSV color space, edge feature, image quality evaluation, color feature, quality fusion

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