Progressive Image Compression With Bandelets

Abstract

Image compression has emerged as a major research area due to the phenomenal growth of applications that generate, process and transmit images. Image compression can be sequential or progressive. Progressive compression techniques generate an embedded bit stream and the fidelity ofthe reconstruction depends on the number of bits received and decoded. Natural images contain edges, geometry, texture and other discontinuities / details that are oriented in various directions. The state-ofthe-art wavelet transform captures point singularities, but not along surfaces with geometric regularity. The second generation discrete wavelet-bandelet transform is proposed to overcome the drawback of wavelets in higher dimensions and capture the geometry in images. The redundancy in the wavelet transform is removed by bandeletization. The wavelet-bandelet coefficients are quantized and encoded using modified bit plane coding and the results have been compared with the existing bit plane coding and the set partitioning in hierarchical trees algorithm. Bandelets produce superior visual quality in the reconstructed image than wavelets. The parameters used for the evaluation of the algorithm are compression ratio, bits per pixel and peak signal-to-noise ratio.