An Approach to Lossy Image Compression Using 1-d Wavelet Transforms

In this paper, an approach to lossy image compression using 1-D wavelet transforms is proposed. The analyzed image is divided in little subimages and each one is decomposed in vectors following a fractal Hilbert curve. A Wavelet Transform is thus applied to each vector and high frequency components are suppressed. The Huffman coding algorithm is then applied in order to reduce image weight. The 64-point vector transforms used in this work allow lower computational time rather than conventional 8x8-matrix transforms. Two different percentages of high frequency coefficients in one and two level wavelet transform are suppressed achieving good compression ratios and SNR. Exploiting the properties of human visual system, simulation results show that image distortion is less appreciated in image sizes 512 by 512 pixels and greater.