Abstract
Digital images are of a great importance in medicine. Efficient and compact storing of the medical digital images represents a major issue that needs to be solved. JPEG lossy compression algorithm is most widely used where better compression to quality ratio can be obtained by selecting appropriate quantization tables. Finding the optimal quantization tables is a hard combinatorial optimization problem and stochastic metaheuristics have been proven to be very efficient for solving such problems. In this paper we propose adjusted bare bones fireworks algorithm for quantization table selection. The proposed method was tested on different medical digital images. The results were compared to the standard JPEG algorithm. Various image similarity metrics were used and it has been shown that the proposed method was more successful.
Keywords
M. Tuba—This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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Tuba, E., Jovanovic, R., Beko, M., Tallón-Ballesteros, A.J., Tuba, M. (2018). Bare Bones Fireworks Algorithm for Medical Image Compression. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_29
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DOI: https://doi.org/10.1007/978-3-030-03496-2_29
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