  Understanding and comparing images for the purposes of data analysis is
currently a very computationally demanding task. A group at Australian National
University (ANU) recently developed open-source code that can detect
fundamental topological features of a grayscale image in a computationally
feasible manner. This is made possible by the fact that computers store
grayscale images as cubical cellular complexes. These complexes can be studied
using the techniques of discrete Morse theory. We expand the functionality of
the ANU code by introducing methods and software for analyzing images encoded
in red, green, and blue (RGB), because this image encoding is very popular for
publicly available data. Our methods allow the extraction of key topological
information from RGB images via informative persistence diagrams by introducing
novel methods for transforming RGB-to-grayscale. This paradigm allows us to
perform data analysis directly on RGB images representing water scarcity
variability as well as crime variability. We introduce software enabling a a
user to predict future image properties, towards the eventual aim of more rapid
image-based data behavior prediction.
