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Proceedings Paper

A comparison study of image spatial entropy
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Paper Abstract

Shannon entropy as a measure of image information is extensively used in image processing applications. This measure requires estimating a high-dimensional image probability density function which poses a limitation from a practical standpoint. A number of approaches have been introduced in the literature for estimating image spatial entropy based on the assumption of Markovianity or homogeneity. This paper provides an overview of these existing approaches and their differences with Shannon entropy. These definitions are compared by applying them to synthesized test images. These images are designed in such a way that the spatial arrangements of pixels are changed without altering the histogram, thus allowing the emphasis to be placed on evaluating image spatial entropy. Furthermore, the computational complexity aspect of the definitions are discussed. The comparison results show that although the definition of image spatial entropy based on Aura Matrix provides the most effective outcome among the existing definitions, there are still deficiencies associated with this definition.

Paper Details

Date Published: 19 January 2009
PDF: 10 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72571X (19 January 2009); doi: 10.1117/12.814439
Show Author Affiliations
Q. R. Razlighi, Univ. of Texas at Dallas (United States)
N. Kehtarnavaz, Univ. of Texas at Dallas (United States)


Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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