Joint codebook design for summation product-code vector quantizers

Author(s):  
W.-Y. Chan ◽  
A. Gersho ◽  
S.-W. Soong
1995 ◽  
Author(s):  
Ioannis Katsavounidis ◽  
C.-C. Jay Kuo ◽  
Zhen Zhang

Author(s):  
Takeshi Miura ◽  
◽  
Kentaro Sano ◽  
Kenichi Suzuki ◽  
Tadao Nakamura

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.


2002 ◽  
Vol 149 (5) ◽  
pp. 299-304 ◽  
Author(s):  
M.-Y. Liu ◽  
H.-W. Tsao

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