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.

Duke Scholars

Author Lawrence Carin Electrical and Computer Engineering