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Channel Optimized Sample Adaptive Product Quantization
 

Summary: Channel Optimized Sample Adaptive
Product Quantization 
Zahir Raza, Fady Alajaji and Tamas Linder
Mathematics and Engineering
Department of Mathematics and Statistics
Queen's University
Kingston, ON, Canada, K7L 3N6
E-mail: zahir,fady,linder@mast.queensu.ca
Abstract
Channel optimized vector quantization (COVQ), as a joint source-channel coding
scheme, has proven to perform well in compressing a source and making the resulting
quantizer robust to channel noise. Unfortunately like its counterpart in the noiseless
channel case, the vector quantizer (VQ), the COVQ encoding complexity is inherently
high. Sample adaptive product quantization was recently introduced by Kim and Shro
to reduce the complexity of the VQ while achieving comparable distortions, even for
moderate quantization dimensions. In this paper, we investigate the SAPQ for the case of
noisy channels and employ the joint source-channel approach of optimizing the quantizer
design by taking into account both source and channel statistics. It is shown that, like
its counterpart in the noiseless case, the channel optimized SAPQ achieves comparable
performance results to the COVQ (within 0.2-1.0 dB), while maintaining considerably

  

Source: Alajaji, Fady - Department of Mathematics and Statistics, Queen's University (Kingston)
Linder, Tamás - Department of Mathematics and Statistics, Queen's University (Kingston)

 

Collections: Engineering