HIERARCHICAL PARTITION PRIORITY WAVELET IMAGE COMPRESSION

Image compression methods for progressive transmission using optimal h ierarchical decomposition, partition priority coding (PPC), and multip le distribution entropy coding (MDEC) are presented, In the proposed c oder, a hierarchical subband/wavelet decomposition transforms the orig inal image. The analysis filter banks are selected to maximize the rep roduction fidelity in each stage of progressive image transmission. An efficient triple-state differential pulse code modulation (DPCM) meth od is applied to the smoothed subband coefficients, and the correspond ing prediction error is Lloyd-Max quantized. Such a quantizer is also designed to fit the characteristics of the detail transform coefficien ts in each subband, which are then coded using novel hierarchical PPC (HPPC) and predictive HPPC (PHPPC) algorithms, More specifically, give n a suitable partitioning of their absolute range, the quantized detai l coefficients are ordered based on both their decomposition level and partition and then are coded along with the corresponding address map , Space filling scanning further reduces the coding cost by providing a highly spatially correlated address map of the coefficients in each PPC partition. Finally, adaptive MDEC is applied to both the DPCM and HPPC/PHPPC outputs by considering a division of the source (quantized coefficients) into multiple subsources and adaptive arithmetic coding based on their corresponding histograms, Experimental results demonstr ate the great performance of the proposed compression methods.