Proceedings Article10.1117/12.217622
Image compression using the W-transform
Jr. William D. Reynolds
- 01 Sep 1995
- Vol. 2569, pp 689-700
TL;DR: The basic theory behind the W-transform is presented and experimental simulations to demonstrate its capabilities are included to show its capabilities as a convenient tool for image compression.
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Abstract: The authors present the W-transform for a multiresolution signal decomposition. One of the differences between the wavelet transform and W-transform is that the W-transform leads to a nonorthogonal signal decomposition. Another difference between the two is the manner in which the W-transform handles the endpoints (boundaries) of the signal. This approach does not restrict the length of the signal to be a power of two. Furthermore, it does not call for the extension of the signal thus, the W-transform is a convenient tool for image compression. They present the basic theory behind the W-transform and include experimental simulations to demonstrate its capabilities.
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Citations
Multirate digital signal processing
S. Biyiksiz
- 01 Mar 1985
TL;DR: This book by Elliott and Rao is a valuable contribution to the general areas of signal processing and communications and can be used for a graduate level course in perhaps two ways.
956
A hybrid W-transform-based coding and its VLSI realization for image compression
Shen-Fu Hsiao,Wen-Chen Huang,Chung-Nan Lee,Cheng-Chung Hsu +3 more
- 02 Jun 1998
TL;DR: In this paper, the authors proposed an image compression algorithm that combines the W-transform, normalized quantization, and entropy coding to enhance the compression ratio, and the proposed algorithm achieves a higher compression rate compared to the JPEG and the other previously proposed Wtransform based method.
References
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Fundamentals of digital image processing
Anil K. Jain
- 03 Oct 1988
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
9K
Image coding using wavelet transform
TL;DR: A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed and it is shown that the wavelet transform is particularly well adapted to progressive transmission.
Wavelets and filter banks: theory and design
Martin Vetterli,Cormac Herley +1 more
TL;DR: The perfect reconstruction condition is posed as a Bezout identity, and it is shown how it is possible to find all higher-degree complementary filters based on an analogy with the theory of Diophantine equations.
Multirate digital signal processing
S. Biyiksiz
- 01 Mar 1985
TL;DR: This book by Elliott and Rao is a valuable contribution to the general areas of signal processing and communications and can be used for a graduate level course in perhaps two ways.
956
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