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 to the same compressed image and solutions to the inverse problem cannot be uniquely determined. Vector quantization is a compression technique that maps an input set of k-dimensional vectors into an output set of k-dimensional vectors, such that the selected output vector is closest to the input vector according to a selected distortion measure. In this paper, we show that adaptive 2D vector quantization of a fast discrete cosine transform of images using Kohonen neural networks outperforms other Kohonen vector quantizers in terms of quality (i.e. less distortion). A parallel implementation of the quantizer on a network of SUN Sparcstations is also presented.