Image Processing. A new Approach via Informational Entropy and Informational Divergence of non Random Functions

Guy Jumarie

Abstract


By combining a maximum conditional entropy principle with a basic equation of (Shannon) information theory, one can obtain a meaningful concept of informational entropy of non random functions. When this entropy is applied to the brightness function of an image, one so has at hand a new tool which provides new approaches to some image processing problems, such as, for instance, image representation, image compression and image similarity. As a by-product, to some extent, this new modelling provides a support to the so-called monkey model of image entropy. But while the latter involves the brightness itself, here, the entropy of the brightness function is expressed in terms of the contrast of the brightness instead of the brightness itself. In this framework, a new concept of informational divergence of an image is obtained, which could be of help in image analysis.


Keywords


information, entropy of non random functions, image entropy, brightness, brightness contrast, image compression, image similarity, image encoding, Shannon information, Renyi information, image cross entropy

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