Comparative study of algorithms for VQ design using conventional and neural-net based approaches

The authors present results of a comparative study of the efficiency of neural-net based approaches (Kohonen and NNVQ) and conventional (LBG and K-means) approaches for vector quantization. They focus on the accuracy and speed of the four methods for the VQ (vector quantization) design problem using two different input sources: a Gaussian Markov source and a speech signal (digit strings). It is shown that the LBG (Y. Linde, A. Buzo, and R. M. Gray 1980) and NNVQ methods achieve more accurate vector quantization than the K-means and Kohonen methods. The NNVQ method offers computational advantages, since the neural-net-based algorithm can be implemented with the use of parallel processors due to its inherent parallelism