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Proceedings Paper

A generalized 2D/3D progressive image coder based on 1D integer wavelet transformation
Author(s): Rongkai Zhao; Michael Gabriel; Geneva G. Belford
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Paper Abstract

Images, 2D or 3D, are usually perceived or analyzed in their respective number of dimensions either in the spatial domain or frequency domain. 3D images such as volumetric data sets are important in many scientific and biomedical fields. To extend a 2D image compression coder to 3D, special care is often required. We are proposing a progressive lossy to lossless image coder that can be extended to multi-dimensions with minimum effort. Hilbert traversal enables us to transform a multi-dimensional signal to a 1D signal, thus 2D/3D images can be compressed in the same way. Only the traversal program needs to be modified for images in different dimensions. Hilbert traversal's locality and slow context change properties render a very compressible 1D signal. After integer wavelet transformation, the resulting wavelet coefficients are rearranged based on our new linearization algorithm. The most important information appears in the front of the data stream. Progressive image encoding/decoding, which is desired by many applications, is possible due to the linearization algorithm. The control data and wavelet coefficients are finally entropy coded to produce a compact data stream. Lossy and lossless image information is embedded in the same data stream.

Paper Details

Date Published: 18 October 2004
PDF: 12 pages
Proc. SPIE 5561, Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications, (18 October 2004); doi: 10.1117/12.558994
Show Author Affiliations
Rongkai Zhao, Univ. of Illinois/Urbana-Champaign (United States)
Michael Gabriel, Univ. of Illinois/Urbana-Champaign (United States)
Geneva G. Belford, Univ. of Illinois/Urbana-Champaign (United States)


Published in SPIE Proceedings Vol. 5561:
Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications
Mark S. Schmalz, Editor(s)

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