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Journal of Electronic Imaging

Compound document compression with model-based biased reconstruction
Author(s): Edmund Yin-Mun Lam
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Paper Abstract

The usefulness of electronic document delivery and archives rests in large part on advances in compression technology. Documents can contain complex layouts with different data types, such as text and images, having different statistical characteristics. To achieve better image quality, it is important to make use of such characteristics in compression. We exploit the transform coefficient distributions for text and images. We show that the scheme in baseline JPEG does not lead to minimum mean-square error if we have models of these coefficients. Instead, we discuss an algorithm designed for this performance that involves first classifying the blocks, and then estimating the parameters to enable a biased reconstruction in the decompression value. Simulation results are shown to validate the advantages of this method.

Paper Details

Date Published: 1 January 2004
PDF: 7 pages
J. Electron. Imaging. 13(1) doi: 10.1117/1.1631317
Published in: Journal of Electronic Imaging Volume 13, Issue 1
Show Author Affiliations
Edmund Yin-Mun Lam, Univ. of Hong Kong (Hong Kong China)

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