Abstract
This paper proposes a hybrid evolutionary algorithm based on an accelerated version of K-means integrated with a modified genetic algorithm (GA) for vector quantization (VQ) codebook optimization. From simulation results involving image compression based on VQ, it is observed that the proposed method leads to better codebooks when compared with the conventional one (GA + standard K-means), in the sense that the former leads to higher peak signal-to-noise ratio (PSNR) results for the reconstructed images. Additionally, it is observed that the proposed method requires fewer GA generations (up to 40%) to achieve the best PSNR results produced by the conventional method.
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Azevedo, C.R.B., Bispo, E.L., Ferreira, T.A.E., Madeiro, F., Alencar, M.S. (2008). An Evolutionary Approach for Vector Quantization Codebook Optimization. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_51
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DOI: https://doi.org/10.1007/978-3-540-87732-5_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
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