An Efficient Method for Computation of Entropy and Joint Entropy of Images

Abstract

This paper proposes an efficient method to compute entropy and joint entropy of images. Entropy of images is used to determine its quality. It is defined as the randomness or uncertainty present in the image. Similarly, joint entropy is a measure of the uncertainty present in the overlapped region of two images. Entropy and joint entropy computations are vital in several image processing applications. Intensity based image registration is done by maximizing the mutual information between two images. Mutual information is nothing but the difference between sum of individual entropies and joint entropy of two images. Image registration has applications, especially in the medical field, e.g. diagnosis and treatment of diseases. The entropy and joint entropy computation methods proposed in this paper are computationally less expensive than the standard methods. Entropy computation takes 78.60% less time than the standard method while computational time of joint entropy is reduced by 83.59%. This increase in efficiency comes at the cost of an error of 1.52% in entropy and 4.54% in joint entropy.