Kernel matching pursuits prioritization of wavelet coefficients for SPIHT image coding

The set partitioning in hierarchical trees (SPIHT), an efficient wavelet-based progressive image-compression scheme, is oriented to minimize the mean-squared error (MSE) between the original and decoded imagery. In this paper, we use the kernel matching pursuits (KMP) method to estimate the importance of each wavelet sub-band for distinguishing between different textures segmented by an HMT mixture model. Before the SPIHT coding, we weight the wavelet coefficients, with the goal of achieving improved image-classification results at low bit rates. A modified SPIHT algorithm is proposed to improve the coding efficiency. The performances of the original SPIHT and the modified SPIHT algorithms are compared.