Detail Preserving Wavelet-Based Compression with Adaptive Context-Based Quantisation

A novel compression method called MBWT (modified basic wavelet technique) for efficient medical image compression is presented. MBWT is based on classic wavelet compression scheme with dyadic decomposition, uniform scalar quantisation and entropy coding of wavelet coefficients. By exploiting of space-frequency data characteristics in wavelet domain, an algorithm of adaptive quantisation and separable context-based binary coding with two (bitewise and wordwise) arithmetic coders are proposed. Also the most suitable filter banks selection is included. Quantisation step size evaluation is done for each subband on the basis of two items: established image quality level and wavelet coefficients variance estimation. Additionally adaptive threshold data selection is performed to reduce of unimportant coefficients in noisy areas. 9-order noncausal context model depicts the importance of the data from spatial correlations. The significance of parent node is also taken into account as a frequency aspect of data dependence. The idea of entropy coding includes three main elements: a) the construction of a decomposition tree with significant and insignificant nodes, b) pruning the tree - modified zerotree construction with four symbols of alphabet, c) separate arithmetic coding of two distinct data streams - a set of significant coefficients and a significant root node map with the signs of significant data. The MBWT algorithm seems to be simpler than the majority of efficient wavelet compression techniques and the effectiveness of our method is competitive with the best algorithms in the literature across diverse classes of medical images. Objective and subjective image quality evaluation supports this conlusion. Significant efficiency improvement over SPIHT algorithm is irrefutable for all images tested.