• DocumentCode
    2816264
  • Title

    Image compression by nonlinear principal component analysis

  • Author

    Yoshioka, Michifumi ; Omatu, Sigeru

  • Author_Institution
    Fac. of Eng., Osaka Prefecture Univ., Japan
  • Volume
    2
  • fYear
    1996
  • fDate
    18-21 Nov 1996
  • Firstpage
    704
  • Abstract
    In recent years, many methods for image compression have proposed, especially JPEG and MPEG have achieved high compression ratio, but these methods can not restore images completely. In these methods image data are reduced in spatial frequency domain according to human eye property. In this study, we have developed a new method to reduce image data especially in noises of image using a neural network. An advantage of this method is to preserve the quality of image by reducing the noise which is independent of original image data
  • Keywords
    data compression; neural nets; noise; statistical analysis; video signal processing; image compression; neural network; noise reduction; nonlinear principal component analysis; Filters; Frequency domain analysis; Humans; Image coding; Image restoration; Neural networks; Noise reduction; Principal component analysis; Source separation; Transform coding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Technologies and Factory Automation, 1996. EFTA '96. Proceedings., 1996 IEEE Conference on
  • Conference_Location
    Kauai, HI
  • Print_ISBN
    0-7803-3685-2
  • Type

    conf

  • DOI
    10.1109/ETFA.1996.573990
  • Filename
    573990