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Attention to Authors

The Volume 1, Number 2, November 2016 issue of IJARCE has been published and hardcopies have been sent to authors. Notice that, arrival process of the hardcopies, to your addresses, will takes between 12 to 30 days depending on your country of origin.

 

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Vector Quantization Using the Hybrid Swarm Intelligence Algorithms

Artical 18 ; volume2 ; number1 ; Page: 05-16 ; PDF Download

Authors

Sorayya Amouei Kamal Mirzaie Fatemeh Saadatjoo Ali Mohammad Latif

Department of Computer Engineering, Science and Art University, Yazd, Iran.

Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran.

Department of Computer Engineering, Science and Art University, Yazd, Iran.

Department of Computer Engineering, Yazd University, Yazd, Iran.

Abstract

Vector Quantization (VQ) was the powerful technique in image compression. Generating a good codebook is
an important part of VQ. There are various algorithms in order to generate an optimal codebook. Recently,
Swarm Intelligence (SI) algorithms were adapted to obtain the near-global optimal codebook of VQ. In this
paper, we proposed a new method based on a hybrid particle swarm optimization (PSO) and firefly
algorithm (FA) to construct the codebook of VQ. The proposed method used PSO algorithm as the initial of
FA to develop the VQ. This method is called PSO-FA model. Experimental results indicate that the proposed
model is faster than FA. Furthermore, the reconstructed images get higher quality than FA, but it is no
significant superiority to the PSO algorithm.
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