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Proceedings Paper

Context-based lossless image compression with optimal codes for discretized Laplacian distributions
Author(s): Ciprian Doru Giurcaneanu; Ioan Tabus; Cosmin Stanciu
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Paper Abstract

Lossless image compression has become an important research topic, especially in relation with the JPEG-LS standard. Recently, the techniques known for designing optimal codes for sources with infinite alphabets have been applied for the quantized Laplacian sources which have probability mass functions with two geometrically decaying tails. Due to the simple parametric model of the source distribution the Huffman iterations are possible to be carried out analytically, using the concept of reduced source, and the final codes are obtained as a sequence of very simple arithmetic operations, avoiding the need to store coding tables. We propose the use of these (optimal) codes in conjunction with context-based prediction, for noiseless compression of images. To reduce further the average code length, we design Escape sequences to be employed when the estimation of the distribution parameter is unreliable. Results on standard test files show improvements in compression ratio when comparing with JPEG-LS.

Paper Details

Date Published: 28 May 2003
PDF: 11 pages
Proc. SPIE 5014, Image Processing: Algorithms and Systems II, (28 May 2003); doi: 10.1117/12.481916
Show Author Affiliations
Ciprian Doru Giurcaneanu, Tampere Univ. of Technology (Finland)
Ioan Tabus, Tampere Univ. of Technology (Finland)
Cosmin Stanciu, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 5014:
Image Processing: Algorithms and Systems II
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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