Efficient Algorithm for Image Compression Using DWT Techniques

Abstract

This paper proposes a method for image compression using discrete wavelet transformation (DWT) techniques. One use of wavelet approximation is in data compression. Like some other transforms, wavelet transforms can be used to transform data, and then encode the transformed data, resulting in effective compression. A related use is that of smoothing/de-noising data based on wavelet coefficient thresholding, also called wavelet shrinkage. By adaptively thresholding the wavelet coefficients that correspond to undesired frequency components smoothing and/or de-noising operations can be performed. Wavelets are functions that are concentrated in time as well as in frequency around a certain point. For practical applications, we choose wavelets which correspond to a so called “multi-resolution analysis” (MRA) due to the reversibility and the efficient computation of the appropriate transform. Wavelets fulfill certain self-similarity conditions. Images are obviously two-dimensional data. To transform images we can use two-dimensional wavelets or apply the one-dimensional transform to the rows and columns of the image successively as separable two-dimensional transform. In most of the applications, where wavelets are used for image processing and compression, the latter choice is taken, because of the low computational complexity of separable transforms.