STATISTICAL MODELING FOR LEARNING VECTOR QUANTIZER CODEBOOK DESIGN IN THE WAVELET DOMAIN

Abstract

A statistical approach for modeling the code vectors designed using a supervised learning neural network is proposed in this paper. Since wavelet-based compression is more robust under transmission and decoding errors, the proposed work is implemented in the wavelet domain. Two crucial issues in compression methods are the coding efficiency and the psycho visual quality achieved while modeling different image regions. In this paper, a high performance wavelet coder which provides a new framework for handling these issues in a simple and effective manner is proposed. First the input image is subjected to wavelet transform. Then the transformed coefficients are subjected to Quantization followed by the well known Huffman Encoder. In the Quantization process, initially a codebook is designed using Learning Vector Quantizer. Since codebook is an essential component for the reconstructed image quality and also to exploit the spatial energy compaction of the codevectors, the codebook is further modeled using Savitzky–Golay polynomial. Experimental results show that the proposed work gives better results in terms of PSNR that are competitive with the state-of-art coders in literature.