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Proceedings Paper

Image compression using sparse colour sampling combined with nonlinear image processing
Author(s): Stephen Brooks; Ian Saunders; Neil A. Dodgson
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Paper Abstract

We apply two recent non-linear, image-processing algorithms to colour image compression. The two algorithms are colorization and joint bilateral filtering. Neither algorithm was designed for image compression. Our investigations were to ascertain whether their mechanisms could be used to improve the image compression rate for the same level of visual quality. Both show interesting behaviour, with the second showing a visible improvement in visual quality, over JPEG, at the same compression rate. In both cases, we store luminance as a standard, lossily compressed, greyscale image and store colour at a very low sampling rate. Each of the non-linear algorithms then uses the information from the luminance channel to determine how to propagate the colour information appropriately to reconstruct a full colour image.

Paper Details

Date Published: 15 February 2007
PDF: 12 pages
Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64920F (15 February 2007); doi: 10.1117/12.703056
Show Author Affiliations
Stephen Brooks, Dalhousie Univ. (Canada)
Ian Saunders, Univ. of Edinburgh (United Kingdom)
Neil A. Dodgson, Univ. of Cambridge (United Kingdom)


Published in SPIE Proceedings Vol. 6492:
Human Vision and Electronic Imaging XII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly, Editor(s)

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