Evolving Better Wavelet Compression Schemes: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „ == Reference == Andreas Klappenecker and Frank U. May: Evolving Better Wavelet Compression Schemes. Wavelet Applications in Signal and Image Processing III, …“) |
(kein Unterschied)
|
Aktuelle Version vom 28. November 2014, 12:54 Uhr
Inhaltsverzeichnis
Reference
Andreas Klappenecker and Frank U. May: Evolving Better Wavelet Compression Schemes. Wavelet Applications in Signal and Image Processing III, Vol. 2569, SPIE, 9-14 July 1995.
DOI
Abstract
Wavelet based compression schemes belong to the general class of transform coding schemes. We show how the genetic programming approach can be used to optimize such a compression scheme in the sense of rate-distortion. The results of optimized wavelet based compression schemes are compared with the JPEG compression standard. A prototype implementation of the method is realized as a distributed, parallel implementation on a heterogeneous Unix network. Keywords: Wavelets, compression, optimization, genetic programming. 1 INTRODUCTION Lossy image data compression is an impressive application of wavelet algorithms. The aim is to implement an efficient compression scheme, which is flexible enough to cover a great variety of bit rates while achieving a minimum of distortion. This goal can only be attained if the scheme is adapted to the human visual system as well as to the image class considered. We show how the genetic programming paradigm 8 can be used to optimize wavelet based compre...
Extended Abstract
Bibtex
Used References
1] R. R. Coifman, Y. Meyer, S. Quake, and M. V. Wickerhauser. Signal processing and compression with wavelet packets. In Y. Meyer and S. Roques, editors, Progress in Wavelet Analysis and Applications, pages 77{93. Ed. Frontieres, 1992.
2] I. Daubechies. Orthonormal bases of compactly supported wavelets. Comm. Pure Appl. Math., 41:909{996, 1988.
3] I. Daubechies. Ten Lectures on Wavelets. CBMS-NSF Reg. Conf. Series Appl. Math. SIAM, 1992.
4] A. Gersho and R. M. Gray. Vector Quantization and Signal Compression. Kluwer Academic Publishers, 1992.
5] Independent JPEG Group. JPEG compression software, release 5beta2. Available on the Internet at ftp://ftp.uu.net/graphics/jpeg.
6] G. Hauske. Systemtheorie der visuellen Wahrnehmung. Teubner, Stuttgart, 1994.
7] M. Holschneider. Wavelets { An Analysis Tool. Oxford University Press, 1995.
8] J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1993. 2nd printing.
9] G. G. Langdon. An introduction to arithmetic coding. IBM J. Res. Devel., 28(2):135{149, 1984.
10] B. Macq. Weighted optimum bit allocation to orthogonal transform of picture coding. IEEE Sel. Areas in Comm., 10(5), 1992.
11] J. L. Mannos and D. J. Sakrisson. The e ects of a visual delity criterion on the encoding of images. IEEE Trans. Inf. Theory, 20(4):525{536, 1974.
12] Y. Meyer. Ondelettes et Operateurs, volume 1. Hermann, Paris, 1990.
13] K. Ramchandran and M. Vetterli. Best wavelet packet bases in a rate-distortion sense. IEEE Trans. Image Proc., 2(2):160{175, 1993.
14] M. J. T. Smith and T. P. Barnwell. Exact reconstruction techniques for tree structured subband coders. IEEE ASSP, 34:434{441, 1986.
15] R. Veldhuis and M. Breeuwer. An Introduction to Source Coding. Prentice Hall, 1993.
16] I. A. Witten, M. N. Radford, and J. G. Cleary. Arithmetic coding for data compression. Comm. ACM, 30(6):520{540, 1987
Links
Full Text
[extern file]
Sonstige Links
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.4667&rank=1
