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A Novel Algorithm for Bi-Level Image Coding and Lossless Compression based on Virtual Ant Colonies

Topics: Artificial Life; Complex Adaptive Systems; Data/image, Feature, Decision and Multilevel Fusion; Human-Machine interfaces, robotics and image processing; Information and Entropy; Information Systems; Intelligent Multi-agent Systems and Intelligent Agents; Multi-Agent Systems

Authors: Matthew Mouring 1 ; Khaldoon Dhou 2 and Mirsad Hadzikadic 3

Affiliations: 1 KPIT Extended PLM, United States ; 2 University of Missouri, United States ; 3 University of North Carolina at Charlotte, United States

ISBN: 978-989-758-297-4

Keyword(s): Ant Colonies, Pheromone, Proximity, Binary Images, Arithmetic Coding.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Artificial Life ; Computational Intelligence ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Evolutionary Computing ; Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Technologies ; Multi-Agent Systems ; Operational Research ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: Ant colonies emerged as a topic of research and they are applied in different fields. In this paper, we develop an algorithm based on the concept of ant colonies and we utilize if for image coding and compression. To apply the algorithm on images, we represent each image as a virtual world which contains food and routes for ants to walk and search for it. Ants in the algorithm have certain type of movements depending on when and where they find food. When an ant finds food, it releases a pheromone, which allows other ants to follow the source of food. This increases the likelihood that food areas are covered. The chemical evaporates after a certain amount of time, which in turn helps ants move to cover another food area. In addition to the pheromone, ants use proximity awareness to detect other ants in the surrounding, which can help ants cover more food areas. When an ant finds food, it moves to that location and the movement and coordinates are recorded. If there is no food, an ant moves randomly to a location in the neighborhood and starts searching. We ran our algorithm on a set of 8 images and the empirical results showed that we could outperform many techniques in image compression including JBIG2. (More)

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Paper citation in several formats:
Mouring, M.; Dhou, K. and Hadzikadic, M. (2018). A Novel Algorithm for Bi-Level Image Coding and Lossless Compression based on Virtual Ant Colonies.In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS, ISBN 978-989-758-297-4, pages 72-78. DOI: 10.5220/0006688400720078

@conference{complexis18,
author={Matthew Mouring. and Khaldoon Dhou. and Mirsad Hadzikadic.},
title={A Novel Algorithm for Bi-Level Image Coding and Lossless Compression based on Virtual Ant Colonies},
booktitle={Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,},
year={2018},
pages={72-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006688400720078},
isbn={978-989-758-297-4},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - A Novel Algorithm for Bi-Level Image Coding and Lossless Compression based on Virtual Ant Colonies
SN - 978-989-758-297-4
AU - Mouring, M.
AU - Dhou, K.
AU - Hadzikadic, M.
PY - 2018
SP - 72
EP - 78
DO - 10.5220/0006688400720078

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