Kohonen neural network for image coding based on iteration transformation theory

Alexandru Bogdan; H. E. Meadows

doi:10.1117/12.130849

16 December 1992 Kohonen neural network for image coding based on iteration transformation theory

Alexandru Bogdan, H. E. Meadows

Author Affiliations +

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130849
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

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 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 its 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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Alexandru Bogdan and H. E. Meadows "Kohonen neural network for image coding based on iteration transformation theory", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130849

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