CODEVECTOR MODELING USING LOCAL POLYNOMIAL REGRESSION FOR VECTOR QUANTIZATION BASED IMAGE COMPRESSION

Abstract

Image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. In this paper, a still image compression scheme driven by Self-Organizing Map with polynomial regression modeling and entropy coding, employed within the wavelet framework is presented. The image compressibility and interpretability are improved by incorporating noise reduction into the compression scheme. The implementation begins with the classical wavelet decomposition, quantization followed by Huffman encoder. The codebook for the quantization process is designed using an unsupervised learning algorithm and further modified using polynomial regression to control the amount of noise reduction. Simulation results show that the proposed method reduces bit rate significantly and provides better perceptual quality than earlier methods.

Keywords

• Wavelet Transform
• Savitzky-Golay Polynomial
• Vector quantization
• Huffman Coding