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Soft Computing
Erschienen in: Soft Computing 9/2015

01.09.2015 | Methodologies and Application

Image quantization using improved artificial fish swarm algorithm

verfasst von: Shaimaa Ahmed El-said

Erschienen in: Soft Computing | Ausgabe 9/2015

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Abstract

Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature.
Vorheriger Artikel An approach to the script discrimination in the Slavic documents
Nächster Artikel Intuitionistic fuzzy random variable and testing hypothesis about its variance

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Metadaten
Titel
Image quantization using improved artificial fish swarm algorithm
verfasst von
Shaimaa Ahmed El-said
Publikationsdatum
01.09.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 9/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1436-0

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