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Vector Quantization based Lossy Image Compression using Wavelets ? A Review

Binit Amin, Patel Amrutbhai

This work informs a survey on vector quantization (VQ) based lossy image compression using wavelets. The objective of image compression is to help in storing the transmitted data in proficient way by decreasing its redundancy. It also involves reducing the size of image data file, while retaining necessary information and maintaining a certain level of quality. Depends on this the image compression is classified into two categories: lossy and lossless image compression. There are many lossy techniques exists for image compression in digital domain, among this wavelet transformation based image compression by using vector quantization (VQ) provides good picture quality and better image compression ratio compared to all other techniques. Vector quantization (VQ) has the potential to greatly reduce the amount of information required for an image because it compresses in vectors which provides better efficiency than compressing in scalars. The most popular algorithm for generating a Vector Quantizer codebook is the Linde-Buzo-Gray (LBG) algorithm or Generalised-Lloyd (GLA) algorithm. The objective is to generate the standard codebook by using some standard training set which is capable of successfully coding images outside of the training set. Vector quantization (VQ) based coded images then encoded for transmission by using different encoding technique like Huffman encoding, Run Length Encoding (RLE) etc.