<title>Neural networks in bandwidth compression</title>

Abstract

The first objective of this study is to analyze the use of neural networks for bandwidth compression. This is achieved by developing a neural network algorithm using the Kohonen self-organization technique to perform vector quantization. The second objective of the study is to combine the neural network vector quantizer with a DPCM encoder for more efficient bandwidth compression. The bandwidth compression techniques are simulated and their performance is evaluated using one-dimensional wideband signals. Vector quantization (VQ) has proved to be an efficient method for bandwidth compression of both one-dimensional signals and imagery data. The reason VQ is not utilized in many practical applications is due to the fact that the performance of VQ becomes superior to other techniques such as transform coding and DPCM only for large vector dimensions. Large vector dimensions also increase the number of computations (per sample) and the memory requirements of the VQ; the increase is an exponential function of the vector dimension. For this reason, in this study VQ has been used in combination with other methods of bandwidth compression so that using a small vector dimension still improves the overall system performance. Neural networks present a parallel approach to data classification that may simplify the architecture of the classifier in the VQ, thus making vector quantizers with large dimensions more practical. In this study, we have developed a neural network algorithm for vector quantization which is based on Kohonen self-organization technique. In the following we discuss the neural network classifiers and their utilization in vector quantization of the DPCM encoders. Simulation results showing the system performance of these systems for one-dimensional modulated signals is presented and the results are discussed.