Advanced Search
Volume 32 Issue 09
Sep.  2006
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2006  >  32(09): 1031-1034.
Tong Yubing, Zhang Qishan, Chang Qing, et al. Image quality assessing model by using neural network and support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(09): 1031-1034. (in Chinese)
Citation: Tong Yubing, Zhang Qishan, Chang Qing, et al. Image quality assessing model by using neural network and support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(09): 1031-1034. (in Chinese)
shu

Image quality assessing model by using neural network and support vector machine

  • 1.

    School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. College of Physics and Electrical & Information, Northwest Normal University, Lanzhou 730070, China

  • Received Date: 29 Nov 2005
  • Publish Date: 30 Sep 2006
    Abstract
  • Pear signal to noise ratio(PSNR) and structure similarity(SSIM) as two indexes describing image quality were used with neural network(NN) and support vector machine(SVM) to set up new effective image quality assessing model. The definition of isolated points and the prediction of isolated points were illuminated. NN was used to obtain the image quality assessing mapping functions and SVM was used to classify the samples into different types. UTexas image database was used in simulation experiment. With the same level of consistency of quality assessing model, the prediction monotonicity of the model is 7.42% higher than PSNR. The root mean square error (rmse) of the model is 36.06% higher than PSNR. The number of isolated points with the new model was reduced and the performance of the model was enhanced. The results from simulation experiment show the model valid. The output of the new model can effectively reflect the image subjective quality.

     

    • FullText(HTML)
  • loading
    • References(1)
  • [1] Daly S. The visible difference predictor:an algorithm for the assessment of image fidelity, digital images and human vision[M]. Massachusetts, U S A:The MIT Press, 1993:179-206 [2] Heeger D J, Teo T C. A model of perceptual image fidelity Proceeding of 1995 Internation Conference of Image Processing. Washington:, 343-345 [3] Watson A B, Solomon J A. Model of visual contrast gain control and pattern masking [J]. Journal of Optical Society of America, 1997,14(9):2379-2391 [4] Vanden C J, Branden Lambrecht, Costantini D M, et al, Quality assessment of motion rendition in video coding [J]. IEEE Trans Circuits and Systems for Video Tech, 1999,9(5):766-782 [5] Zhou Wang, Liang Lu, Alan C Bovik. Video quality assessment using structural distortion measurement Proceeding of 2002 Internation Conference of Image Processing.Rochester,New York:, III-65-68 [6] 佟雨兵,张其善. 视频质量评价方法综述[J].计算机辅助设计与图形学报,2006,18(5):735-741 Tong Yubing, Zhang Qishan. Video quality assessment methods overview[J]. Journal of Computer-Aided Design & Computer Graphics,2006,18(5):735-741(in Chinese) [7] RRNR-TV group test plan. Draft version 1.7 .2004 . http://www.vqeg.org [8] Wang Zhou, Alan C Bovik, Eero P. Simoncelli. handbook of image and video processing[M]. 2 nd ed, New York:Academic Press, 2005 [9] Vladimir N Vapnik. An overview of statistical learning theory [J]. IEEE Transactions on Neural Networks, 1999,10(5):988-999 [10] JPEG-release1_database .2005.http://live.ece.utexas.edu/index.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 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
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(3007) PDF downloads(1237) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return