On entropy-constrained vector quantization using gaussian mixture models

@article{Zhao2008OnEV,
  title={On entropy-constrained vector quantization using gaussian mixture models},
  author={David Zhao and Jonas Samuelsson and Mattias Nilsson},
  journal={IEEE Transactions on Communications},
  year={2008},
  volume={56}
}
A flexible and low-complexity entropy-constrained vector quantizer (ECVQ) scheme based on Gaussian mixture models (GMMs), lattice quantization, and arithmetic coding is presented. The source is assumed to have a probability density function of a GMM. An input vector is first classified to one of the mixture components, and the Karhunen-Loeve transform of the selected mixture component is applied to the vector, followed by quantization using a lattice structured codebook. Finally, the scalar… CONTINUE READING
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