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.