A Novel Agent-Based Modeling Approach for Image Coding and Lossless Compression Based on the Wolf-Sheep Predation Model

Abstract

In this article, the researcher develops an image coding technique which is based on the wolf-sheep predation model. In the design, images are converted to virtual worlds of sheep, routes and wolves. Wolves in this model wander around searching for sheep while the algorithm tracks their movement. A wolf has seven movements which capture all the directions of the wolf. In addition, the researcher introduces one extra move of the wolf the purpose of which is to provide a shorter string of movements and to enhance the compression ratio. The first coordinates and the movements of the wolf are tracked and recorded. Then, arithmetic coding is applied on the string of movements to further compress it. The algorithm was applied on a set of images and the results were compared with other algorithms in the research community. The experimental results reveal that the size of the compressed string of wolf movements offer a higher reduction in space and the compression ratio is higher than those of many existing compression algorithms including G3, G4, JBIG1, JBIG2 and the recent agent-based model of ant colonies.