Multiscale image coding using the Kohonen neural network

Marc Antonini; Michel Barlaud; Pierre Mathieu; J. C. Feauveau

doi:10.1117/12.24101

1 September 1990 Multiscale image coding using the Kohonen neural network

Marc Antonini, Michel Barlaud, Pierre Mathieu, J. C. Feauveau

Proceedings Volume 1360, Visual Communications and Image Processing '90: Fifth in a Series; (1990) https://doi.org/10.1117/12.24101
Event: Visual Communications and Image Processing '90, 1990, Lausanne, Switzerland

Abstract

This paper proposes a new method for image coding involving two steps. First, we use a 'Dual Recursive Wavelet' Transform in order to obtain a set of subclasses of images with better characteristics than the original image (lower entropy, edges discrimination, ... ). Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using the Kohonen Self-Organizing Feature Maps. We compare this training method with the well known LBG algorithm.

Citation Download Citation

Marc Antonini, Michel Barlaud, Pierre Mathieu, and J. C. Feauveau "Multiscale image coding using the Kohonen neural network", Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); https://doi.org/10.1117/12.24101

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