Application of compactly supported wavelets to image compression

William R. Zettler; John C. Huffman; David C. P. Linden

doi:10.1117/12.19505

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1 June 1990 Application of compactly supported wavelets to image compression

William R. Zettler, John C. Huffman, David C. P. Linden

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Proceedings Volume 1244, Image Processing Algorithms and Techniques; (1990) https://doi.org/10.1117/12.19505
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States

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.

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William R. Zettler, John C. Huffman, and David C. P. Linden "Application of compactly supported wavelets to image compression", Proc. SPIE 1244, Image Processing Algorithms and Techniques, (1 June 1990); https://doi.org/10.1117/12.19505

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