A highly efficient chain code for compression using an agent-based modeling simulation of territories in biological beavers.

The accelerated developments in technology led to a tremendous increase in the volumes of data to be transferred and exchanged between various network channels. These advancements create a huge demand for researchers to investigate new data compression techniques. Recent evidence from the literature shows that agent-based modeling is a promising direction to reduce the size of the data and change its original representation. In this article, the objective is to build an agent-based modeling simulation for chain coding and take advantage of it in data compression. Our agent-based model is inspired by the concept of defended territories of biological beavers. To this end, we use the pixel distribution in a bi-level image to construct a virtual environment of agents, add the beavers, and build territories around them. The main idea of defended beaver territories is to allow each beaver to maintain its area and protects it from intruders. To put it another way, defended territories allow beavers to work on different parts of an image while the algorithm tracks and records their movements, as well as manages disputes between them. Our research findings represent a further step towards employing the generated codes of movements in image processing operations other than coding and compression. Additionally, the experimental results showed that the current model was prosperous, and it could outperform many existing image compression techniques, including JBIG family methods. What’s more, paired-samples t-tests reveal that the mean differences between the outcomes of the current approach and each of the other standardized benchmarks we employed in comparison are statistically significant.