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Vector quantization codebook design based on Fish School Search algorithm
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DOI：10.1016/j.asoc.2018.09.025.png)
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Abstract
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Vector Quantization (VQ) has been used in image coding systems since it allows high compression rates. Codebook design can be seen as a high dimensional optimization problem and, in this scenario, swarm intelligence techniques have been used. This paper presents a new VQ codebook design algorithm based on swarm clustering. The method, based on Fish School Search (FSS) algorithm, is introduced. The FSS is embedded in Linde-Buzo-Gray (LBG) algorithm as a swarm clustering method, here called FSS-LBG. Also, a modification in the original FSS breeding operation is proposed in order to favor the exploration ability and, therefore, achieve better results in terms of PSNR of the reconstructed images. Simulation results show gains up to 1.57 dB in terms of PSNR when compared to LBG algorithm for the image Lena at 0.5625 bpp using a codebook of size 512. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Vector quantization
Codebook design
Fish school search
Swarm intelligence
Swarm clustering algorithm
Image compression
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