Satyanarayana Chanagala
Methods of compressing the data prior to storage and/or transmission are of significant practical and commercial interest. Considerable amount of research has been devoted in the last two decades to tackle the problem of image compression. Image compression addresses the problem of reducing the amount of data required to represent a digital image. The underlying basis of the reduction process is the removal of redundant data. From a mathematical viewpoint, this amounts to transforming a 2-D pixel array into a statistically uncorrelated data set. The transformation is applied prior to storage or transmission of the image. At some later time, the compressed image is decompressed to reconstruct the original image or an approximation of it. In this work discrete wavelet transformation is used to compress the images. It is found that the inherent features of discrete wavelet transform have advantages over the discrete cosine transform which make them a better tool for image compression by providing higher compression ratio without affecting much the quality of the reconstructed image.