Modeling of wavelet coefficients in medical image compression

The discrete wavelet transform provides a new framework of multiresolution space-frequency representation. Its preliminary applications in medical image compression are promising. An accurate modeling of the spatial and frequency characteristics of the wavelet coefficients is a key to designing efficient and accurate quantization for wavelet-based source coding. In this study, we investigate the modeling of a finite mixture distribution of the wavelet coefficients, within the context of information theory and statistical model identification. Using a finite generalized Gaussian mixture to model the overall distribution of the coefficients, an unsupervised learning procedure is developed to quantify the histogram through a tripled adaptive algorithm including detection of the number of local kernels, approximation of the shape of local kernels, and estimation of model parameter values. Our preliminary experimental results indicate that the unsupervised and adaptive histogram quantification can efficiently and accurately fit to the overall mixture distribution of the coefficients for any given frequency subband with unknown characteristics.