Image quality assessment algorithm for multi-distorted image

WANG Chun-zhe; LI Jie; LI Ming-jing; GUO Pan

doi:10.3788/YJYXS20153004.0681

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Image quality assessment algorithm for multi-distorted image

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Image quality assessment algorithm for multi-distorted image
- Chinese Journal of Liquid Crystals and Displays Vol. 30, Issue 4, Pages: 681-686(2015)
- 作者机构：
  
  1. 长春理工大学研究生院,吉林长春,130022
  2. 长春大学电子信息工程学院,吉林长春,130022
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20153004.0681
  CLC： TN141
- Received：17 September 2014，
  
  Revised：27 October 2014，
  
  Published：05 August 2015
- 稿件说明：
移动端阅览
艾雨, 蒋学兵. 残影不良分析及改善对策研究[J]. 液晶与显示, 2015,30(4): 566-570

AI Yu, JIANG Xue-bing. Analysis and research of slow charging[J]. , 2015,30(4): 566-570
艾雨, 蒋学兵. 残影不良分析及改善对策研究[J]. 液晶与显示, 2015,30(4): 566-570 DOI： 10.3788/YJYXS20153004.0681.

AI Yu, JIANG Xue-bing. Analysis and research of slow charging[J]. , 2015,30(4): 566-570 DOI： 10.3788/YJYXS20153004.0681.

摘要

针对现有评价方法不适合于多扭曲失真图像这一问题

本文展开了对多扭曲失真图像评价方法的研究。在分析图像的边缘信息和奇异值向量对视觉特征的表征能力的基础上

提出一种基于边缘信息奇异值分解的图像质量评价算法。首先

利用Sobel算子提取参考图像及失真图像视觉敏感的边缘信息

再对两图像的边缘信息进行奇异值分解

利用奇异值向量之间的夹角来描述失真图像的畸变程度。最后

采用LIVE数据库中的450张多扭曲的失真图像验证该文算法

并与MSE、PSNR、SSIM、CSSIM等算法进行了对比。实验结果表明

该文算法对多扭曲失真图像的质量评价具有更高的稳定性

主客观评价的一致性较传统评价方法更好。通过对比时间效率

该方法基本上满足实际需求

具有更高的适用性。

Abstract

Due to the problem that the existing method of objective quality assessment algorithm is not suitable for multi-distorted images

the quality assessment method for multi-distorted images was studied in this paper.On the basis of analyzing capability that the singular vector and edge information representing visual feature

an algorithm of image quality assessment based on the singular value decomposition of edge information was put forward.Firstly

the edge information of the reference image and distorted image which is sensitive to visual information was extracted by using the Sobel operator.Secondly

the singular value of the edge information of two image was decomposed

then the angle between the singular value was calculated to describe the quality of the distorted image.Finally

the algorithm applied in this paper are verified by more than 450 distorted images in LIVE Multiply Distorted Image Quality Database

and the algorithm was compared with the MSE

PSNR

SSIM

CSSIM method.Experimental results show that the algorithm is more consistent with human subject scores and has greater stability for multi-distorted image than traditional methods.Through comparison with the time efficiency

the proposed algorithm can basically meet the practical demand

and the algorithm is more usability.

关键词

Keywords

references

Lin W, Kuo C.Perceptual visual quality metrics: a survey [J].Journal of Visual Communication and Image Representation, 2011, 22(4): 297-312.[2] 赵梦,韦学辉. 一种基于主成分分析法的图像质量评价方法 [J]. 杭州电子科技大学学报, 2012, 32(3): 41-44. Zhao M, Wei X H.An image quality evaluation method based on principal component analysis [J].Hangzhou University of Electronic Science and Technology Journals, 2012, 32(3): 41-44.(in Chinese)[3] MaL, Li S N, King N N.Reduced-reference image quality assessment in reorganized DCT domain [J].Signal Processing: Image Communication, 2013, 28(8): 884-902.[4] Li J, Wu K Z, Zhang X M, et al. Image quality assessment based on multi-channel regional mutual information [J].AEU -International Journal of Electronics and Communications, 2012, 66(9):784-787.[5] KalitkinN N, Golovanov R V.Smoothed gradients criterion for image quality assessment [J].Doklady Mathematics,2013, 88(1): 495-498.[6] 钱方, 郭劲, 孙涛,等. 基于小波加权的激光干扰效果评估 [J]. 液晶与显示, 2013,28 (5): 781-787. Qian F,Guo J,Sun T,et al.Assessment of laser-dazziing effects based on weighted wavelet transforms [J].Chinese Journal of Liquid Crystals and Displays, 2013,28 (5): 781-787.(in Chinese)[7] 徐云生, 尹东. 一种基于Contourlet变换的图像质量评价算法 [J]. 电子技术, 2010,47(7):23-26. Xu Y S,Ying D.An image quality assessment algorithm based on contourlet transform [J].Electronic Technology, 2010,47(7):23-26.(in Chinese)[8] 姚军财. 基于人眼对比度敏感视觉特性的图像质量评价方法 [J]. 液晶与显示, 2011, 26(3):390-396. Yao J C.Image quality assessment method based on contrast sensitivity characteristics of human vision system [J].Chinese Journal of Liquid Crystals and Displays, 2012, 27(9): 935-947.(in Chinese)[9] Maria G M, Chaminda T H, Barbara V.Image quality assessment based on edge preservation [J].Signal Processing: Image Communication, 2012, 27(8):875-882.[10] 骞森,朱剑英. 基于奇异值分解的图像质量评价 [J]. 东南大学学报, 2006, 36(4):643-646. Qian S, Zhu J Y.Image quality assessment based on singular value decomposition [J].Journal of Southeast University, 2006, 36(4):643-646.(in Chinese)[11] Sheikh H R, Wang Z, Cormack L, et al. LIVE image quality assessment database release 2[OL].http://live.ece.utexas.edu/research/quality/live_multidistortedimage.htm.

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