Multi-iteration wavelet zero-tree coding for image compression

Abstract

Here, we present a modification of Shapiro's embedded zerotree wavelet algorithm (EZW) for image codec. Shapiro's technique is based on the wavelet transform and on the self-similarity inherent in images. In the EZW, the wavelet transform (WT) coefficients, which provide a multiresolution representation of the image, are arranged according to their significance across scales using a small symbol set (zerotree (ZT) coding). An analysis of the symbol entropy shows that better compression rates can be obtained when two or more iterations of the original algorithm are combined. Consequently, we proposed a modification of Shapiro's original algorithm which we called multi-iteration EZW designed to optimise the combination of ZT and Huffman coding. We studied the behaviour of the multi-iteration algorithm in terms of image quality and bit-rate for natural and medical images. Our findings show that for a given image quality the multi-iteration algorithms and particularly the two-iteration EZW produce lower bit-rates than Shapiro's. In addition, we suggest that the idea of multi-iteration can be generalised to other techniques based on ZT coding.