Image compression by cellular neural networks

Péter L. Venetianer, T. Roska

Research output: Contribution to journalArticle

111 Citations (Scopus)

Abstract

This paper demonstrates how the cellular neuralnetwork universal machine (CNNUM) architecture can be applied to image compression. We present a spatial subband image-compression method well suited to the local nature of the CNNUM. In case of lossless image compression, it outperforms the JPEG image-compression standard both in terms of compression efficiency and speed. It performs especially well with radiographical images (mammograms); therefore, it is suggested to use it as part of a cellular neural/nonlinear (CNN)-based mammogram-analysis system. This paper also gives a CNN-based method for the fast implementation of the moving pictures experts group (MPEG) and joint photographic experts group (JPEG) moving and still image-compression standards.

Original languageEnglish
Pages (from-to)205-215
Number of pages11
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume45
Issue number3
DOIs
Publication statusPublished - 1998

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Cellular neural networks
Image compression

Keywords

  • Cellular neural networks
  • Data compression
  • Image coding
  • Signal processing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Image compression by cellular neural networks. / Venetianer, Péter L.; Roska, T.

In: IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 45, No. 3, 1998, p. 205-215.

Research output: Contribution to journalArticle

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