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Proceedings Paper

Application of compactly supported wavelets to image compression
Author(s): William R. Zettler; John C. Huffman; David C. P. Linden
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Paper Abstract

Multilevel unitary wavelet transform methods for image compression are described. The sub-band decomposition preserves geometric image structure within each sub-band or level. This yields a multilevel image representation. The use of orthonormal bases of compactly supported wavelets to represent a discrete signal in 2 dimensions yields a localized representation of coefficient energy. Subsequent coding of the multiresolution representation is achieved through techniques such as scalar/vector quantization, hierarchical quantization, entropy coding, and non-linear prediction to achieve compression. Performance advantages over the Discrete Cosine Transform are discussed. These include reduction of errors and artifacts typical of Fourier-based spectral methods, such as frequency-domain quantization noise and the Gibbs phenomenon. The wavelet method also eliminates distortion arising from data blocking. The paper includes a quick review of past/present compression techniques, with special attention paid to the Haar transfOrm, the simplest wavelet transform, and conventional Fourier-based subband coding. Computational results are presented.

Paper Details

Date Published: 1 June 1990
PDF: 11 pages
Proc. SPIE 1244, Image Processing Algorithms and Techniques, (1 June 1990); doi: 10.1117/12.19505
Show Author Affiliations
William R. Zettler, Aware, Inc. (United States)
John C. Huffman, Aware, Inc. (United States)
David C. P. Linden, Aware, Inc. (United States)

Published in SPIE Proceedings Vol. 1244:
Image Processing Algorithms and Techniques
Robert J. Moorhead; Keith S. Pennington, Editor(s)

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