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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

JPEG Quantization Table Optimization by Guided Fireworks Algorithm

verfasst von : Eva Tuba, Milan Tuba, Dana Simian, Raka Jovanovic

Erschienen in: Combinatorial Image Analysis

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel An Image Texture Analysis Method for Minority Language Identification
Nächstes Kapitel Shape Matching for Rigid Objects by Aligning Sequences Based on Boundary Change Points
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Metadaten
Titel
JPEG Quantization Table Optimization by Guided Fireworks Algorithm
verfasst von
Eva Tuba
Milan Tuba
Dana Simian
Raka Jovanovic
Copyright-Jahr
2017
Buch
Combinatorial Image Analysis

Print ISBN: 978-3-319-59107-0

Electronic ISBN: 978-3-319-59108-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-59108-7_23

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