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	Volume 1, Issue 3 Pg 189 - 283 (November 2007)
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	Volume 1, Issue 1 Pg 1 - 104 (March 2007)

Advances in Computer Science and Engineering

Advances in Computer Science and Engineering
Volume 1, Issue 1, Pages 89 - 104 (March 2007)

IMAGE COMPRESSION USING VECTOR QUANTIZER BASED ON ANTMGLA
V. S. Jayanthi (India) and A. Shanmugam (India)

Abstract:
Image compression is the application of data compression on digital images. Vector quantization (VQ) has found to be efficient data compression technique for wide range of applications. In VQ, minimization of Mean Square Error (MSE) between codebook vectors and training vectors is a non-linear problem. Traditional GLA algorithm used for designing the codebooks for Vector Quantizer converges to a local minimum, which depends on the initial codebook. Another problem associated with GLA is the Empty Voronoi region problem. Ant colony algorithm is a population-based meta-heuristic search approach that has been receiving increasing attention in the recent years. Ant colony algorithm is inspired by the behavior of real ant colonies. It has shown to be successful and popular for solving optimization problems. In this paper we present a new hybrid approach in which Ant colony algorithm is combined with modified GLA to design a codebook for vector quantization. Simulation results indicate that ANTMGLA has better performance in designing the optimal codebook for Vector Quantizer than other algorithms. The Peak Signal to Noise Ratio (PSNR) is used as an objective measure of reconstructed image quality.

Keywords and phrases: vector quantization, ant colony optimization, the global optimum image compression, codebook design.

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