The SPMP algorithm: An image coding method based on SPIHT and matching pursuit

To make full use of the structural characteristics of original images to improve reconstructed images' quality, this paper proposes a novel hierarchical image encoding method based on set partitioning in hierarchical trees (SPIHT) and matching pursuit (MP) algorithm, named the SPMP algorithm. This new method divides the original images into a smooth layer in low frequency and a detail layer in high frequency by using the Laplacian Pyramid algorithm. In the smooth layer, it uses the discrete wavelet to transform the images from the space domain to the frequency domain, and then it encodes the coefficients in the frequency domain by using the SPIHT algorithm. The method adopts the matching pursuit (MP) algorithm to encode the detail layer based on the clone selection algorithm. The experimental results demonstrate that when using the SPMP algorithm, the output bitstream is embedded with the progressive peak signal-to-noise ratio (PSNR), and the reconstructed images quality is significantly better than that obtained using the wavelet transform encoding, even more obviously under the high compression ratio.