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The likelihood term in restoration of transform-compressed imageryFormat | Member Price | Non-Member Price |
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Paper Abstract
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.
Paper Details
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)
PDF: 11 pages
Proc. SPIE 5299, Computational Imaging II, (21 May 2004); doi: 10.1117/12.525409
Show Author Affiliations
Mark A. Robertson, Air Force Research Lab. (United States)
Published in SPIE Proceedings Vol. 5299:
Computational Imaging II
Charles A. Bouman; Eric L. Miller, Editor(s)
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