Improving Image Vector Quantization with a Genetic Accelerated K-Means Algorithm

Authors:
Carlos R. Azevedo;Tiago A. Ferreira;Waslon T. Lopes;Francisco Madeiro
Affiliations:
Center for Science and Technology, Catholic University of Pernambuco, Brazil;Department of Statistics and Informatics, Federal Rural University of Pernambuco, Brazil;School of Electrical Engineering, AREA 1 --- College of Science and Technology, Brazil;Center for Science and Technology, Catholic University of Pernambuco, Brazil
Venue:
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Year:
2008

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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.