Rigau, JaumeFeixas, MiquelSbert, MateuLaszlo Neumann and Mateu Sbert and Bruce Gooch and Werner Purgathofer2013-10-222013-10-2220053-905673-27-41816-0859https://doi.org/10.2312/COMPAESTH/COMPAESTH05/177-184In this paper, we introduce a new information-theoretic approach to study the complexity of an image. The new framework we present here is based on considering the information channel that goes from the histogram to the regions of the partitioned image, maximizing the mutual information. Image complexity has been related to the entropy of the image intensity histogram. This disregards the spatial distribution of pixels, as well as the fact that a complexity measure must take into account at what level one wants to describe an object. We define the complexity by using two measures which take into account the level at which the image is considered. One is the number of partitioning regions needed to extract a given ratio of information from the image. The other is the compositional complexity given by the Jensen-Shannon divergence of the partitioned image.Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computing Methodologies]: Computer GraphicsPicture/ Image Generation; I.4.0 [Computing Methodologies]: Image Processing and Computer VisionImage Processing Software; I.4.6 [Computing Methodologies]: Image Processing and Computer Vision Segmentation