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Erschienen in:

2018 | OriginalPaper | Buchkapitel

Bare Bones Fireworks Algorithm for Medical Image Compression

verfasst von : Eva Tuba, Raka Jovanovic, Marko Beko, Antonio J. Tallón-Ballesteros, Milan Tuba

Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2018

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel A Study of Fuzzy Clustering to Archetypal Analysis

Nächstes Kapitel EMnGA: Entropy Measure and Genetic Algorithms Based Method for Heterogeneous Ensembles Selection

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Titel: Bare Bones Fireworks Algorithm for Medical Image Compression
verfasst von: Eva Tuba
Raka Jovanovic
Marko Beko
Antonio J. Tallón-Ballesteros
Milan Tuba
Verlag: Springer International Publishing
Buch: Intelligent Data Engineering and Automated Learning – IDEAL 2018
Print ISBN: 978-3-030-03495-5

Electronic ISBN: 978-3-030-03496-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-03496-2_29

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