GMM-Based Entropy-Constrained Vector Quantization

Abstract

In this paper, we present a scalable entropy-constrained vector quantizer based on Gaussian mixture models (GMMs), lattice quantization, and arithmetic coding. We assume that the source has a probability density function of a GMM. The scheme is based on a mixture component classifier, the Karhunen Loeve transform of the component, followed by a lattice quantization. The scalar elements of the quantized vector are entropy coded using a specially designed arithmetic coder. The proposed scheme has a computational complexity that is independent of rate, and quadratic with respect to vector dimension. The design is flexible and allows for adjusting the desired target rate on-the-fly. We evaluated the performance of the proposed scheme on speech-derived source vectors. It was demonstrated that the proposed scheme outperforms a fixed-rate GMM based vector quantizer, and performs closely to the theoretical optimum.