Abstract
Vector quantization (VQ) is a commonly used method in the compression of images and signals. The quality of VQ-encoded images heavily depends on the quality of the codebook. Conventional codebook training techniques are all based on the LBG (Linde-Buzo-Gray) method. However, LBG-based methods are noise sensitive and are not able to handle clusters of different shapes, sizes, and densities. In this paper, we propose a density-based clustering method that can identify arbitrary data shapes and exclude noises for codebook training. In order to rapidly approach an optimal solution, an improved version of a genetic algorithm is designed that demonstrates efficient initialization of codewords selection, crossover, and mutation. The experiments show that the proposed method is more robust in generating a common codebook than other LBG-based methods.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chang, CC., Lin, CY. (2006). Density-Based Image Vector Quantization Using a Genetic Algorithm. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_29
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DOI: https://doi.org/10.1007/978-3-540-69423-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69421-2
Online ISBN: 978-3-540-69423-6
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