Progressive Wavelet Image Coding Based on a Conditional Probability Model

Abstract

We present a wavelet image coder based on an explicit model of the conditional statistical relationships between coefficients in different subbands. In particular, we construct a parameterized model for the conditional probability of a coefficient given coefficients at a coarser scale. Subband coefficients are encoded one bitplane at a time using a non-adaptive arithmetic encoder. The overall ordering of bitplanes is determined by the ratio of their encoded variance to compressed size. We show rate-distortion comparisons of the coder to first and second-order theoretical entropy bounds and the EZW coder [1]. The coder is inherently embedded, and should prove useful in applications requiring progressive transmission. Orthonormal wavelet decompositions have proven to be extremely effective for image compression [2, 3, 4, 5, 1]. We believe there are several statistical reasons for this success. Similar to the Fourier transform, wavelets are quite good at decorrelating the second-order statistics of ..