An improved EZW algorithm based on set partitioning in hierarchical trees using wavelet regularity

This paper presents an improved embedded zerotree wavelet (EZW) coding algorithm, which makes use of the wavelet regularity to derive a classification criterion of wavelet coefficients in spatial-orientation hierarchical trees. Variations of the EZW algorithm discussed in the open literature have proposed some modifications in the process of exploiting the similarity of coefficients through the scales, however, not defining a figure of merit to measure such a similarity. Simulation results achieved from the coding of well-known images in the literature, for several bit rates, show a better performance of the proposed algorithm in both PSNR and subjective terms, as compared with EZW and SPIHT algorithms.