Abstract
Our work is concerned with quantization in image compression. The task of quantization is to approximate transformed data of original images so that the images can be efficiently stored. In previous studies of abstraction, reformulation and approximation (AR&A), the notions seems to be mainly used to improve computational efficiency. Although the purposes might seem to be quite different, an important theme is shared in each case: “how to create a good AR&A for a problem setting we are concerned with?”
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
J. Knipe, X. Li and B. Han: “An Improved Lattice Vector Quantization Based Scheme for Wavelet Compression”, IEEE Transactions on Signal Processing, Vol. 46, No. 1, pp. 239–242, 1998.
P. Shelley: “New Techniques in Wavelet Image Compression”, Master Thesis, Department of Computing Science, University of Alberta, 2001.
J. H. Conway and N. J. A. Sloane: “Sphere Packings, Lattices and Groups”, Springer-Verlag, 1988.
Y. Linde, A. Buzo and R. Gray: “An Algorithm for Vector Quantizer Design”, IEEE Transactions on Communications, Vol. COM-28, No. 1, pp. 84–95, 1980.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Okubo, Y., Li, X. (2002). Learning Semi-lattice Codebooks for Image Compression. In: Koenig, S., Holte, R.C. (eds) Abstraction, Reformulation, and Approximation. SARA 2002. Lecture Notes in Computer Science(), vol 2371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45622-8_36
Download citation
DOI: https://doi.org/10.1007/3-540-45622-8_36
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43941-7
Online ISBN: 978-3-540-45622-3
eBook Packages: Springer Book Archive