A. Bogdan, H. E. Meadows. Kohonen neural network for image coding based on iteration transformation theory. In Proceedings from SPIE Neural and Stochastic Methods in Image and Signal Processing, Volume 1766, Pages 425-436, 1992.
Iterated Transformation Theory (ITT), also known as Fractal Coding, is a relatively new block compression method which removes redundancies between different scale representations of the uncompressed signal. In ITT coding we are looking for a piecewise continuous mapping from the space of all images with the same support with the same support onto itself which has a close approximation of the desired image as a unique fixed point. The mapping is then the code for the image, and for decoding we iterate the mapping on any initial image, orders of magnitude faster than encoding. We have reduced the computational load of finding the piecewise continuous transformation by using a Self-Organizing Feature Map (SOFM) artificial Neural Network which finds similar features in different resolution representations of the image. The patterns are mapped onto a two-dimensional array of formal neurons forming a code book similar to vector quantization (VQ) coding. We use the (SOFM) ordering properties by searching for mapping not only to the best feature match neuron but also to ist neighbors in the network. In this paper we describe the ITT - SOFM algorithm and its software implementation with application to image coding of still gray images. Computer simulations show compression results comparable to or better than state-of-the-art VQ coders, and computational complexity better than most of the well known clustering algorithms.
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@InProceedings{BoMe92,
Author = {Bogdan, A. and Meadows, H. E.},
Title = {Kohonen neural network for image coding based on iteration transformation theory},
BookTitle = {Proceedings from SPIE Neural and Stochastic Methods in Image and Signal Processing},
Volume = {1766},
Pages = {425--436},
Year = {1992}
}