Structured Gaussian mixture model based product VQ

In this paper, the Gaussian mixture model (GMM) based parametric framework is used to design a product vector quantization (PVQ) method that provides rate-distortion (R/D) performance optimality and bitrate scalability. We use a GMM consisting of a large number of Gaussian mixtures and invoke a block isotropic structure on the covariance matrices of the Gaussian mixtures. Using such a structured GMM, we design an optimum and bitrate scalable PVQ, namely an split (SVQ), for each Gaussian mixture. The use of an SVQ allows for a trade-off between complexity and R/D performance that spans the two extreme limits provided by an optimum scalar quantizer and an unconstrained vector quantizer. The efficacy of the new GMM based PVQ (GM-PVQ) method is demonstrated for the application of speech spectrum quantization.