<title>Efficient vector quantization technique for images</title>

Abstract

Image transmission is a very effective method of conveying information for a large number of applications. Vector quantization (VQ) is the most computational demanding technique that uses a finite set of vectors as its mapping space. It was shown that VQ is capable of producing good reconstructed image quality. However, it has the problem of computation complexity in the codebook creation part. We have found that neural networks is a fast alternative approach to create the codebooks. Neural network appears to be particularly well-suited for VQ applications. In neural networks approach we use parallel computing structures. Also, most neural network learning algorithms are adaptive and can be used to produce effective scheme for training the vector quantizer. A new method for designing the vector quantizer called Concentric-Shell Partition Vector Quantization is introduced. It first partitions the image vector space into concentric shells and then searches for the smallest possible codebook to represent the image vector space, while adhering to the visual perceptive qualities such as edges and textures in the image representation. In this paper, we are presenting neural networks using the frequency sensitive learning algorithm and the concentric-shell partitioning approach for VQ. This new technique will show the simplicity of the neural network model while retaining the computational advantages.