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Proceedings Paper

Adaptive vector quantization for binary images
Author(s): Rustin W. Allred; Richard W. Christiansen; Douglas M. Chabries
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Paper Abstract

This paper describes a vector quantization variant for lossy compression of binary images. This algorithm, adaptive binary vector quantization for binary images (ABVQ), uses a novel, doubly-adaptive codebook to minimize error while typically achieving compression higher than is achieved by lossless techniques. ABVQ provides sufficient fidelity to be used on text images, line drawings, graphics, or any other binary (two-tone, or bi-level) images. Experimental results are included in the paper.

Paper Details

Date Published: 28 December 2000
PDF: 8 pages
Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); doi: 10.1117/12.411536
Show Author Affiliations
Rustin W. Allred, Texas Instruments Inc. (United States)
Richard W. Christiansen, Brigham Young Univ. (United States)
Douglas M. Chabries, Brigham Young Univ. (United States)

Published in SPIE Proceedings Vol. 4115:
Applications of Digital Image Processing XXIII
Andrew G. Tescher, Editor(s)

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