Image data compression based on discrete wavelet transformation

Image compression research aims at reducing the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. New algorithms for image compression based on wavelets have been developed. These methods have resulted in practical advances such as: lossless and lossy compression, progressive transmission by pixel, accuracy and resolution, region of interest coding and others. The various wavelet based image coding schemes are discussed in this paper. Each of these schemes finds use in different applications owing to their unique characteristics. The methods of lossy compression that we concentrated on are the following: the EZW algorithm, the SPIHT algorithm, the WDR algorithm, and the ASWDR algorithm. These are relatively recent algorithms which achieve some of the lowest errors per compression rate and highest perceptual quality yet reported. After describing these algorithms in detail, we show and discuss the experimental results obtained for three different types of images. We also showed that some important features of image, such as standard deviation and mean pixel intensity values, only slightly change after compression. This fact is very important in medical image compression