Image and Document Compression

Compound document compression with model-based biased reconstruction

[+] Author Affiliations
Edmund Y. Lam

University of Hong Kong, Department of Electrical and Electronic Engineering, Hong Kong E-mail: elam@eee.hku.hk

J. Electron. Imaging. 13(1), 191-197 (Jan 01, 2004). doi:10.1117/1.1631317
History: Received Jan. 29, 2003; Revised Jun. 19, 2003; Revised Jul. 31, 2003; Accepted Aug. 12, 2003; Online March 01, 2004
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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. © 2004 SPIE and IS&T.

© 2004 SPIE and IS&T

Citation

Edmund Y. Lam
"Compound document compression with model-based biased reconstruction", J. Electron. Imaging. 13(1), 191-197 (Jan 01, 2004). ; http://dx.doi.org/10.1117/1.1631317


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