|Table of Contents|

SVD-Based Gray-Scale Image Quality AssessmentAlgorithms in the SSIM Perspective(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年01期
Page:
73-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
SVD-Based Gray-Scale Image Quality AssessmentAlgorithms in the SSIM Perspective
Author(s):
Liu DajinYe JianbingLiu Jiajun
Taizhou Institute of Science and Technology,Nanjing University of Science and Technology,Taizhou 225300,China
Keywords:
image quality assessmentsingular value decompositionstructural similarity
PACS:
TP391.41
DOI:
10.3969/j.issn.1001-4616.2017.01.011
Abstract:
Image quality assessment is a fundamental problem in the field of image processing. Singular value decomposition properties for images and structural similarity-based image quality assessment are deeply discussed. According to both the theoretical and empirical analysis,the drawbacks of current two categories of algorithms are pointed out. Image quality assessment algorithms that based on singular value decomposition are explained from the perspective of the structural similarity. In addition,possible improvement strategies for the current methods are also discussed.

References:

[1] SHNAYDERMAN A,GUSEV A,ESKICIOGLU A M. An SVD-based grayscale image quality measure for local and global assessment[J]. IEEE transactions on image processing,2006,15(2):422-429.
[2]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(自然科学版),2006,36(4):643-646.
[3]YANG C A,KAVEH M. Image quality assessment using singular vectors[C]//Proceedings of SPIE 7529. California:International Society for Optics and Photonics,2010.
[4]NARWARIA M,LIN W. Scalable image quality assessment based on structural vectors[C]//Proceedings of IEEE International Workshop on Multimedia Signal Processing. Reo De Janeiro:IEEE,2009.
[5]WANG R,CUI Y,YUAN Y. Image quality assessment using full-parameter singular value decomposition[J]. Optical engineering,2011,50(5):057005.
[6]奚晓婷,张建秋. 一种奇异值与其向量联合评估图像质量的测度[J]. 复旦学报(自然科学版),2012,51(1):83-90.
[7]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.
[8]李航,路羊,崔慧娟,等. 基于频域的结构相似度的图像质量评价方法[J]. 清华大学学报(自然科学版),2009,49(4):559-562.
[9]吕丹,毕笃彦. 基于结构相似的DCT域图像质量评价[J]. 吉林大学学报(工学版),2011,41(6):1 771-1 776.
[10]叶盛楠,苏开娜,肖创柏,等. 基于结构信息提取的图像质量评价[J]. 电子学报,2008,36(5):856-861.
[11]杨春玲,陈冠豪,谢胜利. 基于梯度信息的图像质量评判方法的研究[J]. 电子学报,2007,35(7):1 313-1 317.
[12]苗莹,易三莉,贺建峰,等. 结合梯度信息的特征相似性图像质量评估[J]. 中国图象图形学报,2015,20(6):749-755.
[13]高全学,梁彦,潘泉,等. SVD用于人脸识别存在的问题及解决方法[J]. 中国图象图形学报,2006,11(12):1 784-1 791.
[14]XIAO L,WEI Z,YE J. Comments on“Robust embedding of visual watermarks using discrete wavelet transform and singular value decomposition”and theoretical analysis[J]. Journal of electronic imaging,2008,17(4):040501.
[15]肖亮,叶建兵,韦志辉. 一类基于SVD的数字水印虚警分析与改进算法[J]. 南京理工大学学报(自然科学版),2010,34(2):227-231.
[16]张飞艳,谢伟,陈荣元,等. 基于视觉加权的奇异值分解压缩图像质量评价测度[J]. 电子与信息学报,2010,32(5):1 061-1 065.
[17]SHEIKH H R,WANG Z,CORMACK L,et al. LIVE image quality assessment database release 2[DB/OL]. [2015-03-15].http://live.ece.utexas.edu/research/quality.

Memo

Memo:
-
Last Update: 1900-01-01