Um Algoritmo Memético Para A Otimização De Quantizadores Vetoriais

This paper presents a Memetic Algorithm (MA) for optimizing vector quantizers. The proposed MA updates the codebooks produced at each generation of a modified Genetic Algorithm (GA) by applying iterations of an accelerated version of the Linde-Buzo-Gray (LBG) algorithm. From simulation results involving image compression, it is observed that the proposed MA leads to better codebooks, in a smaller number of generations, when compared to the GA guided by the standard LBG, leading to better peak signal-to-noise results for the reconstructed images. Finally, the complexity of the search for the nearest neighbour in the proposed MA is reduced by the usage of a Partial Distance Search (PDS).

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