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2016 | OriginalPaper | Buchkapitel

Modified Firefly Algorithm (MFA) Based Vector Quantization for Image Compression

verfasst von : Karri Chiranjeevi, Uma Ranjan Jena, B. Murali Krishna, Jeevan Kumar

Erschienen in: Computational Intelligence in Data Mining—Volume 2

Verlag: Springer India

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Abstract

Firefly algorithm optimization is based on the attractiveness/brightness of the firefly. In firefly algorithm, a lighter (lesser fitness function) firefly move towards the brighter firefly (higher fitness function) with amplitude proportional to Euclidean distance between the lighter and brighter firefly. If no such brighter firefly is found then it moves randomly is search space. This random move causes chance of decrement in brightness of the brighter firefly depending on the direction in which it is move. We proposed a modified firefly algorithm in which movement of brighter fireflies is towards the direction of brightness instead of random move. If this direction of brightness is not in the process then firefly is in same position. We call this novel algorithm as MFA-LBG. Experimental results shows that modified firefly algorithm reconstructed image quality and fitness function value is better than the standard firefly algorithm (FA-LBG) and LBG algorithms. It is observed that that modified firefly algorithm convergence time is less than the standard firefly algorithm.

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Metadaten
Titel
Modified Firefly Algorithm (MFA) Based Vector Quantization for Image Compression
verfasst von
Karri Chiranjeevi
Uma Ranjan Jena
B. Murali Krishna
Jeevan Kumar
Copyright-Jahr
2016
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-2731-1_35