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Proceedings Paper

Effective wavelet-based compression method with adaptive quantization threshold and zerotree coding
Author(s): Artur Przelaskowski; Marian Kazubek; Tomasz Jamrogiewicz
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Paper Abstract

Efficient image compression technique especially for medical applications is presented. Dyadic wavelet decomposition by use of Antonini and Villasenor bank filters is followed by adaptive space-frequency quantization and zerotree-based entropy coding of wavelet coefficients. Threshold selection and uniform quantization is made on a base of spatial variance estimate built on the lowest frequency subband data set. Threshold value for each coefficient is evaluated as linear function of 9-order binary context. After quantization zerotree construction, pruning and arithmetic coding is applied for efficient lossless data coding. Presented compression method is less complex than the most effective EZW-based techniques but allows to achieve comparable compression efficiency. Specifically our method has similar to SPIHT efficiency in MR image compression, slightly better for CT image and significantly better in US image compression. Thus the compression efficiency of presented method is competitive with the best published algorithms in the literature across diverse classes of medical images.

Paper Details

Date Published: 6 October 1997
PDF: 9 pages
Proc. SPIE 3229, Multimedia Storage and Archiving Systems II, (6 October 1997); doi: 10.1117/12.290355
Show Author Affiliations
Artur Przelaskowski, Warsaw Univ. of Technology (Poland)
Marian Kazubek, Warsaw Univ. of Technology (Poland)
Tomasz Jamrogiewicz, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 3229:
Multimedia Storage and Archiving Systems II
C.-C. Jay Kuo; Shih-Fu Chang; Venkat N. Gudivada, Editor(s)

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