PARALLEL 1D AND 2D VECTOR QUANTIZERS USING A KOHONEN NEURAL-NETWORK

The process of reconstructing an original image from a compressed one is a difficult problem, since a large number of original images lead t o the same compressed image and solutions to the inverse problem canno t be uniquely determined. Vector quantization is a compression techniq ue that maps an input set of k-dimensional vectors into an output set of k-dimensional vectors, such that the selected output vector is clos est to the input vector according to a selected distortion measure. In this paper, we show that adaptive 2D vector quantization of a fast di screte cosine transform of images using Kohonen neural networks outper forms other Kohonen vector quantizers in terms of quality (i.e. less d istortion). A parallel implementation of the quantizer on a network of SUN Sparcstations is also presented.