@inproceedings{04032fbf5d214e2cbe32e9f44474491d,
title = "Bare bones fireworks algorithm for medical image compression",
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 = "Bare bones fireworks algorithm, Compression, JPEG, Medical image processing, Optimization, Quantization tables",
author = "Eva Tuba and Raka Jovanovic and Marko Beko and Tall{\'o}n-Ballesteros, {Antonio J.} and Milan Tuba",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 ; Conference date: 21-11-2018 Through 23-11-2018",
year = "2018",
doi = "10.1007/978-3-030-03496-2_29",
language = "English",
isbn = "9783030034955",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "262--270",
editor = "David Camacho and Paulo Novais and Tall{\'o}n-Ballesteros, {Antonio J.} and Hujun Yin",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings",
address = "Germany",
}