Image compression using the wavelet transform and context-based arithmetic coding

In this paper, we propose an image compression method using the wavelet transform and context-based arithmetic coding exploiting bit plane correlation. The proposed method, decomposes an image into several subband images using the discrete wavelet transform; the transform coefficients in each subband are classified into two classes; each subband image is then quantized; a "shuffling" process is applied to quantized wavelet coefficients; and finally arithmetic coding using the optimum context is carried out for bit plane coding of each subband. The performance improvement of the proposed method turns out to be mainly due to the "shuffling" process and the optimum context for arithmetic coding. Experimental results show that the proposed coder is similar to or superior to well-known existing coders, e.g. EZW and SPIHT, for images with low correlation.