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Image compression by cellular neural networks

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2 Author(s)
Venetianter, P.L. ; Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary ; Roska, T.

This paper demonstrates how the cellular neural-network 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

Published in:

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on  (Volume:45 ,  Issue: 3 )

Date of Publication:

Mar 1998

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