• DocumentCode
    3489429
  • Title

    Predictive vector quantization using neural networks

  • Author

    Hashemi, M.R. ; Yeap, T.H. ; Panchanathan, S.

  • Author_Institution
    Dept. of Electr. Eng., Ottawa Univ., Ont., Canada
  • Volume
    2
  • fYear
    1995
  • fDate
    5-8 Sep 1995
  • Firstpage
    834
  • Abstract
    Proposes a new predicative vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self organized feature map is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. The proposed technique provides a superior coding performance
  • Keywords
    multilayer perceptrons; prediction theory; self-organising feature maps; vector quantisation; video coding; Kohonen selforganized feature map; coding performance; image compression; multilayer perceptron; neural networks; predictive vector quantization; video compression; Computer networks; Image coding; Image reconstruction; Image storage; Laboratories; Neural networks; Pulse modulation; Speech; Transmitters; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering, 1995. Canadian Conference on
  • Conference_Location
    Montreal, Que.
  • ISSN
    0840-7789
  • Print_ISBN
    0-7803-2766-7
  • Type

    conf

  • DOI
    10.1109/CCECE.1995.526425
  • Filename
    526425
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3489429