Analysis of image informativeness measures

Shannon entropy has been commonly used to quantify the image informativeness. The main drawback of this measure is that it does not take into account the spatial distribution of pixels. In this paper, we analyze four information-theoretic measures that overcome this limitation. Three of them (entropy rate, excess entropy, and erasure entropy) consider the image as a stationary stochastic process, while the fourth (partitional information) is based on an information channel between image regions and histogram bins. Experimental results, applied to natural and synthetic images, show the performance of these measures to characterize several informativeness aspects of an image. We also analyze their behavior under some image effects such as blurring, contrast change, and noise.

[1]  Mateu Sbert,et al.  An information theoretic framework for image segmentation , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[2]  Mateu Sbert,et al.  Toward Auvers Period: Evolution of van Gogh's Style , 2010, CAe.

[3]  David McMahon.  A Brief Introduction to Information Theory , 2008 .

[4]  Mateu Sbert,et al.  Image Segmentation Using Information Bottleneck Method , 2009, IEEE Transactions on Image Processing.

[5]  Raymond W. Yeung,et al.  Information Theory and Network Coding , 2008 .

[6]  Zhou Wang,et al.  Information Content Weighting for Perceptual Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[7]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[8]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  J. Crutchfield,et al.  Structural information in two-dimensional patterns: entropy convergence and excess entropy. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Mateu Sbert,et al.  Image Segmentation Using Excess Entropy , 2009, J. Signal Process. Syst..

[11]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .