A neural network structure for vector quantizers

A variable perturbation method for codebook design in vector quantization (VQ) is proposed. The resulting codebook can be used as the initial codebook in the implementation of the LBG (Line, Buzo, Gray, 1990) VQ algorithm. The proposed method is based on the concept of entropy implemented as a learning algorithm for a feedforward neural network. Such a neural network with the proposed learning algorithm can construct a codebook for input vectors with an unknown distribution without memorizing long training data. >