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JACIII Vol.9 No.2 pp. 166-174
doi: 10.20965/jaciii.2005.p0166
(2005)

Paper:

A Competitive Learning Algorithm with Controlling Maximum Distortion

Takeshi Miura, Kentaro Sano, Kenichi Suzuki, and Tadao Nakamura

Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai 980-8579, Japan

Received:
October 31, 2004
Accepted:
December 25, 2004
Published:
March 20, 2005
Keywords:
vector quantization, optimal codebook design, competitive learning, maximum-distortion control
Abstract

Vector quantization with an optimal codebook is attractive for lossy data compression. So far, a number of codebook design algorithms have been proposed to minimize the mean square error, MSE. However, these algorithms have a problem that MSE minimization sometimes causes an unacceptable maximum-distortion, which is very important in several applications. This paper proposes a competitive learning algorithm with controlling maximum distortion that designs a codebook giving a maximum distortion within a given error bound. The proposed algorithm assigns a code vector to an input vector with a too large distortion. Experimental results showed that the algorithm has both maximum-distortion control and MSE minimization capability.

Cite this article as:
Takeshi Miura, Kentaro Sano, Kenichi Suzuki, and Tadao Nakamura, “A Competitive Learning Algorithm with Controlling Maximum Distortion,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.2, pp. 166-174, 2005.
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