A Competitive Learning Algorithm with Controlling Maximum Distortion

2005 ◽  
Vol 9 (2) ◽  
pp. 166-174
Author(s):  
Takeshi Miura ◽  
◽  
Kentaro Sano ◽  
Kenichi Suzuki ◽  
Tadao Nakamura
Keyword(s):  
Data Compression ◽  
Vector Quantization ◽  
Learning Algorithm ◽  
Competitive Learning ◽  
Mean Square ◽  
Code Vector ◽  
Codebook Design ◽  
Distortion Control ◽  
Large Distortion ◽  
The Mean

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.

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