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Proceedings Paper

The normalized compression distance and image distinguishability
Author(s): Nicholas Tran
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Paper Abstract

We use an information-theoretic distortion measure called the Normalized Compression Distance (NCD), first proposed by M. Li et al., to determine whether two rectangular gray-scale images are visually distinguishable to a human observer. Image distinguishability is a fundamental constraint on operations carried out by all players in an image watermarking system. The NCD between two binary strings is defined in terms of compressed sizes of the two strings and of their concatenation; it is designed to be an effective approximation of the noncomputable but universal Kolmogorov distance between two strings. We compare the effectiveness of different types of compression algorithms? in predicting image distinguishability when they are used to compute the NCD between a sample of images and their watermarked counterparts. Our experiment shows that, as predicted by Li's theory, the NCD is largely independent of the underlying compression algorithm. However, in some cases the NCD fails as a predictor of image distinguishability, since it is designed to measure the more general notion of similarity. We propose and study a modified version of the NCD to model the latter, which requires that not only the change be small but also in some sense random with respect to the original image.

Paper Details

Date Published: 12 February 2007
PDF: 11 pages
Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64921D (12 February 2007); doi: 10.1117/12.704334
Show Author Affiliations
Nicholas Tran, Santa Clara Univ. (United States)

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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