评价彩色图像质量的四元数矩阵最大奇异值方法

王宇庆; 朱明

doi:10.3788/OPE.20132102.0469

您当前的位置：

首页 >

文章列表页 >

评价彩色图像质量的四元数矩阵最大奇异值方法

信息科学 | 更新时间：2020-08-12

- 评价彩色图像质量的四元数矩阵最大奇异值方法
- Max singular value method of quaternion matrix for evaluating color image quality
- 光学精密工程 2013年21卷第2期页码：469-478
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所中国科学院航空光学成像与测量重点实验室,吉林长春,130033
- 作者简介：
- 基金信息：
  
  中国博士后科学基金()
- DOI：10.3788/OPE.20132102.0469
  中图分类号： TP391.4
- 收稿日期：2012-02-21，
  
  修回日期：2012-05-13，
  
  网络出版日期：2013-02-23，
  
  纸质出版日期：2013-02-15
- 稿件说明：
移动端阅览
王宇庆朱明. 评价彩色图像质量的四元数矩阵最大奇异值方法[J]. 光学精密工程, 2013,21(2): 469-478

WANG Yu-qing ZHU Ming. Max singular value method of quaternion matrix for evaluating color image quality[J]. Editorial Office of Optics and Precision Engineering, 2013,21(2): 469-478
王宇庆朱明. 评价彩色图像质量的四元数矩阵最大奇异值方法[J]. 光学精密工程, 2013,21(2): 469-478 DOI： 10.3788/OPE.20132102.0469.

WANG Yu-qing ZHU Ming. Max singular value method of quaternion matrix for evaluating color image quality[J]. Editorial Office of Optics and Precision Engineering, 2013,21(2): 469-478 DOI： 10.3788/OPE.20132102.0469.

摘要

针对传统的图像质量评价方法忽略颜色信息以及与人眼感知信息一致性差的问题，提出了一种全面利用彩色图像颜色信息，突出人眼敏感图像结构的彩色图像质量客观评价方法。将图像中人眼敏感的结构分为细节，亮度和颜色3方面因素，将四元数矩阵作为载体，构造了一种用于彩色图像质量评价的四元数矩阵，并对其进行奇异值分解。将最大奇异值作为度量图像结构相似性的主要参数，通过分析图像结构差异映射图谱得到了最终的量化评价结果。采用LIVE数据库中包含5种失真类型的982张测试图片验证了提出的算法，得到的交叉失真实验非线性拟合均方根误差（RMSE）值为9.176，Spearman等级相关系数（SROCC）值为0.929 6，而结构相似度（SSIM）方法的RMSE值为9.299，SROCC值为0.925 6。试验结果表明，该方法采用四元数矩阵描述彩色图像的结构信息，考虑了彩色图像的多方面结构特征，与人眼视觉感知特性的一致性优于传统方法。

Abstract

A new color image assessment method is proposed to solve the problem of neglecting color information and a poor consistent behavior with the human visual system in traditional image quality assessment methods. The human eye sensitive image structure is enhanced and full color information is used in this method. Three important parts are taken into account in this structure

which are detail information

luminance information and color information. Quaternion is taken as a tool to perform the task. A quaternion matrix is constructed to evaluate color image quality. Then singular value decomposition is performed on the quaternion matrix. Max singular value is used to describe image structure information. Numerical results are obtained by using distortion map. 982 images in LIVE database including five types of distortion are used to investigate the behaviors of the proposed method. The nonlinearity property of the proposed method in the cross-distortion experiment is that the Root Mean Square Error(RMSE) value is 9.176

and Spearman Rank Order Correlation Coefficient(SROCC) value is 0.929 6

however

those of from Structural Similarity Index( SSIM) method are 9.299 and 0.925 6

respectively. The results show that proposed method is more consistent than the traditional methods because of the considering of more image properties and using a quaternion matrix.

关键词

Keywords

references

PARVEZ SAZZAD Z M, KAWAYOKE Y, HORITA Y. No reference image quality assessment for JPEG2000 based on spatial features [J].Signal Processing: Image Communication, 2008, 23(4):257–268.[2]ZHANG J, LE THINH M. A new no-reference quality metric for JPEG2000 images[J]. IEEE Transactions on Consumer Electronics, 2010, 56(2):743-750.[3]BABUA R V,SURESH S, PERKISC A. No-reference JPEG-image quality assessment using GAP-RBF [J]. Signal Processing, 2007, 87 (6):1493-1503.[4]范媛媛,沈湘衡,桑英军.基于对比度敏感度的无参考图像清晰度评价[J]. 光学精密工程,2011,19(10):2485-2493. FAN Y Y, SHEN X H,SANG Y J. No reference image sharpness assessment based on contrast sensitivity [J]. Opt. Precision Eng., 2011,19(10):2485-2493. (in Chinese)[5]SURESH S, BABU R V, KIM H J. No-reference image quality assessment using modified extreme learning machine classifier [J]. Applied Soft Computing, 2009,9(2):541-552.[6]SHEN J, LI Q, ERLEBACHER G. Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000,and JPEG images[J].IEEE Transactions Image Processing, 2011,20(8):2089-2098.[7]ANGELA D, LI Z P, MAURO B. A full-reference quality metric for geometrically distorted images[J]. IEEE Transactions on Image Processing,2010,19(4):867-881.[8]迟学芬,韩昌元.基于信息理论的采样成像系统评价方法[J].光学精密工程,2003,11(2):207-211. CHI X F,HAN C Y.Assessment of sampled imaging system based on information-theory [J].Opt. Precision Eng., 2003, 11(2):207-211. (in Chinese)[9]马冬梅,陈土泉,刘志祥,等.位相复原技术在光学成像质量测评中的应用[J].光学精密工程,2009,17(12):2912-2918. MA D M,CHEN T Q,LIU Z X, et al..Application of phase retrieval technology in evaluation of optical imaging system [J]. Opt. Precision Eng.,2009,17(12):2912-2918. (in Chinese)[10]WANG Z, BOVIK A C.A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3) :181-184.[11]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.[12]FERNANDEZ M, LARABI C, BRINGIER M C, et al.. Spatio-temporal characteristics of the human color perception for digital quality assessment[C]. International Symposium on Signals, Circuits and Systems, New York,2005:203-206.[13]MAURO B, FRANCO B, ALESSIA D R. HVS modelling for quality evaluation of art images [C]. IEEE 14th International Conference on Digital Signal Processing, Santorini, Greece, 2002:91-94.[14]SANTIAGO A F, RAUL S J E, CARLOS A L,et al.. Image quality assessment based on local variance [C]. Proceedings of 28th IEEE Annual International Conference on Engineering in Medicine and Biology Society. New York City, USA, 2006:4815-4818.[15]王宇庆,刘维亚,王勇.一种基于局部方差和结构相似度的图像质量评价方法[J].光电子·激光,2008,19(11):1546-1553.WANG Y Q, LIU W Y, WANG Y. Image quality assessment based on local variance and structure similarity [J]. Journal of Optoelectronics·Laser,2008, 19(11):1546-1553. (in Chinese)[16]ALEKSANDR S, ALEXANDER G, AHMET M E. An SVD-based grayscale image quality measure for local and global assessment [J]. IEEE Transactions on Image Processing, 2006,15(2):422-429.[17]NARWARIA, M, LIN W S. Scalable image quality assessment based on structural vectors[C]. IEEE International Workshop on Multimedia Signal Processing,Rio de Janeiro, Brazil,2009:1-6.[18]袁飞,黄联芬,姚彦.基于视觉掩盖效应和奇异值分解的图像质量评测方法[J].光学精密工程,2008,16(4):706-713. YUAN F , HUANG L F , YAO Y. Image quality evaluation based on visual masking effect and singular value decomposition [J]. Opt. Precision Eng.,2008,16(4):706-713. (in Chinese)[19]NARWARIA M, LIN W S. Objective image quality assessment based on support vector regression[J].IEEE Transactions on Neural Networks,2010,21(3):515-519.[20]ALESSIO D A, ANTONIO M, FABRIZIO R, et al.. A vector approach for image quality assessment and some metrological considerations[J]. IEEE Transactions on Instrumentation and Measurement,2009, 58(1):14-25.[21]GAO X B, LU W, TAO D C, et al.. Image quality assessment based on multiscale geometric analysis [J]. IEEE Transactions on Image Processing,2009,18(7):1409-1423.[22]BEGHDADI A, PESQUEST-POPESCU B. A new image distortion measure based on wavelet decomposition [C]. Seventh International Symposium on Signal Processing and Its Applications,Paris, France ,2003:485-488.[23]SHEIKH H R, WANG Z, CORMACK L, et al.. LIVE image quality assessment database release 2[OL].http://live.ece. utexas .edu/research/quality.[24]SHEIKH H R, SABIR M F, BOVIK A C. A statistical evaluation of recent full reference image quality assessment algorithms [J]. IEEE Transactions on Image Processing, 2006,15(11):3441-3452.[25]HAMILTON W R. On quaternions[C]. Proceeding of the Royal Irish Academy,1844:1-16.[26]PEI S C, CHENG C. A novel block truncation coding of color images by using quaternion moment preserving principle[J]. IEEE Transactions on Communications,1997,45(5):583-595.[27]ITU-R Recommendation BT.601-5: Studio encoding parameters of digital television for standard4:3 and widescreen 16:9 aspect ratios [R].1995,Section 3.5.[28]ZHANG F.Quaternion and matrices of quaternion[J].Lin.Alge.Appl,1997,215:21-57.[29]NICOLAS L B, JEROME M.Singular value decomposition of quatemion matrices:a new tool for vector-sensor signal processing[J].Signal Processing,2004,84(7):1177-1199．[30]ROHALY A M, LIBERT J, CORRIVEAU P, et al..Final report from the video quality experts group on the validation of bjective models of video quality assessment[OL].http://www.its.bldrdoc.gov/vqeg/projects/frtv-phaselI/downloads/ VQEGII Final Report, 2000.

浏览量

448

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于奇异值分解的激光雷达湍流预警算法

结合局部特征的无参考彩色图像质量评价

基于局部方差和互信息的融合图像质量评价

融合变分偏微分方程的单幅彩色图像去雾

刀具磨损的自动检测及检测系统