Computation of image spatial entropy using quadrilateral Markov random field.

Publication Type	Academic Article
Authors	Razlighi Q, Kehtarnavaz N, Nosratinia A
Journal	IEEE Trans Image Process
Volume	18
Issue	12
Pagination	2629-39
Date Published	08/11/2009
ISSN	1941-0042
Abstract	Shannon entropy is a powerful tool in image analysis, but its reliable computation from image data faces an inherent dimensionality problem that calls for a low-dimensional and closed form model for the pixel value distributions. The most promising such models are Markovian, however, the conventional Markov random field is hampered by noncausality and its causal versions are also not free of difficulties. For example, the Markov mesh random field has its own limitations due to the strong diagonal dependency in its local neighboring system. A new model, named quadrilateral Markov random field (QMRF) is introduced in this paper in order to overcome these limitations. A property of QMRF with neighboring size of 2 is then used to decompose an image prior into a product of 2-D joint pdfs in which they are estimated using a joint histogram under the homogeneity assumption. In addition, the paper includes an extension of the introduced method to the computation of image spatial mutual information. Comparisons on synthesized images as well as two applications with real images are presented to motivate the developments in this paper and demonstrate the advantages in the performance of the introduced method over the existing ones.
DOI	10.1109/TIP.2009.2029988
PubMed ID	19674952