Abstract
In this paper, vector quantizer optimization is accomplished by a hybrid evolutionary method, which consists of a modified genetic algorithm (GA) with a local optimization module given by an accelerated version of the K-means algorithm. Simulation results regarding image compression based on VQ show that the codebooks optimized by the proposed method lead to reconstructed images with higher peak signal-to-noise ratio (PSNR) values and that the proposed method requires fewer GA generations (up to 40%) to achieve the best PSNR results produced by the conventional GA + standard K-means approach. The effect of increasing the number of iterations performed by the local optimization module within the proposed method is discussed.
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Keywords
- Genetic Algorithm
- Average PSNR
- Codebook Size
- Conventional Genetic Algorithm
- Genetic Algorithm Generation
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Azevedo, C.R.B., Ferreira, T.A.E., Lopes, W.T.A., Madeiro, F. (2008). Improving Image Vector Quantization with a Genetic Accelerated K-Means Algorithm. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_7
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DOI: https://doi.org/10.1007/978-3-540-88458-3_7
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