JDP_V1_N2_RP1 S. Sridhar P. Rajesh Kumar K.V. Ramanaiah Journal on Digital Signal Processing 2322–0368 1 2 1 9 Bi-Orthogonal, Coiflet, Daubechies, MSE, PSNR, Symlets Wavelet transforms are set of mathematical functions that represent image as a sum of wavelet functions with different locations and scales. Wavelet transformations provide information in both frequency domain and spatial domain as well, standard de-facto images of varying sizes are subjected to two level decomposition using wavelet filter functions like Haar, Daubechies, Biorthogonal, Coiflets and Symlets etc. The transformed approximation and detail coefficients, typically infinite precision real numbers are then quantized such that the more important coefficients are represented with higher accuracy while those with less accuracy are neglected. In quantization input values are mapped to output values, based on particular threshold levels. The quantized coefficients are further coded in a bit stream using recursive splitting Huffman encoding. This study evaluates and compares the merits of selected Wavelet transform techniques for different filter functions graphically to discuss important features of wavelets in image compression. Objective fidelity metrics Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR) obtained are shown graphically. April - June 2013 Copyright © 2013 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=2326