Progressive Wavelet Image Coding Based on a Conditional Probability Model

R W Buccigrossi and E P Simoncelli

Published in ICASSP, vol.IV pp. 2957--2960, Apr 1997.
© IEEE Signal Processing Society
DOI: 10.1109/ICASSP.1997.595412

This paper has been superseded by:
Image compression via joint statistical characterization in the wavelet domain
by R W Buccigrossi and E P Simoncelli.


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    • 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. The coder is inherently embedded, and should prove useful in applications requiring progressive transmission.
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