A novel vector quantizer (VQ) design algorithm for noisy channels is presented in this paper. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, performs the codeword training using an optimal fuzzy clustering technique where the channel noise is taken into account. In the existing crisp channel-optimized VQ (CCOVQ) design algorithms, the quantization accuracy is traded for less sensitivity to channel noise. However, because of utilizing the optimal fuzzy clustering process for VQ design, the FCOVQ algorithm can effectively reduce the sensitivity to channel noise while maintaining the quantization accuracy. Therefore, given the same noisy channel, the FCOVQ can have better rate-distortion performance than that of the CCOVQ techniques.
[1]
Allen Gersho,et al.
Pseudo-Gray coding
,
1990,
IEEE Trans. Commun..
[2]
Nariman Farvardin,et al.
On the performance and complexity of channel-optimized vector quantizers
,
1991,
IEEE Trans. Inf. Theory.
[3]
Nariman Farvardin,et al.
A study of vector quantization for noisy channels
,
1990,
IEEE Trans. Inf. Theory.
[4]
N.B. Karayiannis,et al.
Fuzzy vector quantization algorithms and their application in image compression
,
1995,
IEEE Trans. Image Process..
[5]
Wen-Jyi Hwang,et al.
A fuzzy entropy-constrained vector quantizer design algorithm and its applications to image coding
,
1999
.