Volume 4 Issue 5
Oct.  2011
Turn off MathJax
Article Contents
WANG Yu-qing. Application of local variance in image quality assessment[J]. Chinese Optics, 2011, 4(5): 531-536.
Citation: WANG Yu-qing. Application of local variance in image quality assessment[J]. Chinese Optics, 2011, 4(5): 531-536.

Application of local variance in image quality assessment

  • Received Date: 11 Jul 2011
  • Rev Recd Date: 13 Aug 2011
  • Publish Date: 25 Oct 2011
  • The local variance distribution of a gray level image is taken as an important characteristic to express image structural information, and the Singular Value Decomposition(SVD) is performed on a local variance distribution matrix. The angle between the singular vectors of the reference image and distorted image is used to measure the structural similarity of the two images, and then the image quality assessment is achieved. Experimental result shows that the local variance distribution can emphasize the structural information. It is better consistent with human visual perception characteristics and the assessment results are superior to those from Mean Square Error(MSE), Peak Signal to Noise(PSNR), Structure Similarity(SSIM) and SVD methods based on pixel value distribution.

     

  • loading
  • [1] ESKICIOGLU A M,FISHER P S. A survey of image quality measures for gray scale image compression. Proceeding of 1993 Space and Earth Science Data Compression Workshop,San Diego,USA,1993. [2] WANG ZH,BOVIK A C. A universal image quality index[J]. IEEE Signal Proc. Let.,2002,9(3):181-184 [3] WANG ZH,BOVIK A C,SHEIKH H R,et al.. Image quality assessment:from error visibility to structural similarity[J]. IEEE T. Image Process.,2004,13(4):600-612 [4] LEE Y H,PARK S Y. A study of convex concave edges and edge-enhancing operators based on the Laplacian[J]. IEEE T. Circuits Syst.,1990,37(7):940-946. [5] RAMPONI G. A cubic unsharp masking technique for contrast enhancement[J]. Signal Process.,1998,67(2):211-222 [6] 袁晓松,王秀坛,王希勤. 基于人眼视觉特性的自适应的图像增强算法的研究[J]. 电子学报 ,1999,27(4):63-65. YUAN X S,WANG X T,WANG X Q. An adaptive image enhancement algorithm based on human visual properities[J]. Acta Electronica Sinica,1999,27(4):63-65.(in Chinese) [7] SEZAN M I,YIP K-L,DALY S J. Uniform perceptual quantization:application to digital radiography[J]. IEEE T. Syst.,Man Cyb.,1987,17(4):622-634. [8] JING XING. An image processing model of contrast perception and discrimination of the human visual system. 2002 International Symposium Society for Information Display,Boston,Massachusetts,USA,May 2002. [9] BARNI M,BARTOLIN I F,ROSA A De. HVS modelling for quality evaluation of art images. IEEE 14th International Conference on Digital Signal Processing,Santorini,Greece,July 2002. [10] WESTEN S J P,LAGENDIJK R L,BIEMOND J. Perceptual image quality based on a multiple channel HVS model. IEEE 1995 International Conference on Acoustics,Speech,and Signal Processing,Detroit,MI,USA,1995. [11] FERNANDEZ-MALOIGNE C,LARABI M-C,BRINGIER B,et al.. Spatio-temporal characteristics of the human color perception for digital quality assessment. IEEE Signals,Circuits and Systems ISSCS 2005 International Symposium,New York,USA,14-15 July 2005. [12] BEGHDADI A,PESQUEST-POPESCU B. A new image distortion measure based on wavelet decomposition. IEEE 1995 International Conference on Acoustics,Speech,and Signal Processing,Paris,France,2003. [13] AJA-FERNANDEZ S,ESTEPAR R S J,ALBEROLA-LOPEZ C,et al.. Image quality assessment based on local variance. Proceedings of 28th IEEE EMBS Annual International Conference. New York,USA,2006. [14] GONZALEZ R C,WOODS R E. Digital Image Processing[M]. New York:Addison-Wesley,1992. [15] 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. QIAN S,ZHU J Y. Image quality measure using singular value decomposition[J]. J. Southeast University(Natural Science Edition),2006,36(4):643-646.(in Chinese) [16] TAUBMAN D S,MARCELLIN M W. JPEG2000:Image Compression Fundamentals,Standards,and Practice[M]. Norwell:Kluwer,2001.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Article views(3888) PDF downloads(1675) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return