Compression of imagery by quantization of the data's transform coefficients introduces an error in the imagery upon decompression. When processing compressed imagery, often a likelihood term is used to provide a statistical description of how the observed data are related to the original noise-free data. This work derives the statistical relationship between compressed imagery and the original imagery, which is found to be embodied in a (in general) non-diagonal covariance matrix. Although the derivations are valid for transform coding in general, the work is motivated by considering examples for the specific cases of compression using the discrete cosine transform and the discrete wavelet transform. An example application of motion-compensated temporal filtering is provided to show how the presented likelihood term might be used in a restoration scenario.

Date Published: 21 May 2004
PDF: 11 pages
Proc. SPIE 5299, Computational Imaging II, (21 May 2004); doi: 10.1117/12.525409

Published in SPIE Proceedings Vol. 5299:
Computational Imaging II
Charles A. Bouman; Eric L. Miller, Editor(s)