@inproceedings{b842ec4c0b0545a696e57bba4d9f7f48,
title = "JPEG quantization table optimization by guided fireworks algorithm",
abstract = "Digital images are very useful and ubiquitous, however there is a problem with their storage because of their large size and memory requirement. JPEG lossy compression algorithm is prevailing standard that solves that problem. It facilitates different levels of compression (and the corresponding quality) by using recommended quantization tables. It is possible to optimize these tables for better image quality at the same level of compression. This presents a hard combinatorial optimization problem for which stochastic metaheuristics proved to be efficient. In this paper we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for quantization table optimization. We tested the proposed approach on standard benchmark images and compared results with other approaches from literature. By using various image similarity metrics our approach proved to be more successful.",
keywords = "Fireworks algorithm, Image processing, JPEG algorithm, Quantization tables, Swarm intelligence",
author = "Eva Tuba and Milan Tuba and Dana Simian and Raka Jovanovic",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th International Workshop on Combinatorial Image Analysis, IWCIA 2017 ; Conference date: 19-06-2017 Through 21-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59108-7_23",
language = "English",
isbn = "9783319591070",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "294--307",
editor = "Barneva, {Reneta P.} and Brimkov, {Valentin E.} and Brimkov, {Valentin E.}",
booktitle = "Combinatorial Image Analysis - 18th International Workshop, IWCIA 2017, Proceedings",
address = "Germany",
}