High-Rate Training of Gaussian Mixture Vector Quantizers
Ethan R. Duni, Bhaskar D. Rao · 2006
Summary form only given. This paper discusses the design of fixed-rate Gaussian mixture vector quantizers (GMVQs) under input-weighted squared error distortion measures. The goal is to select the system parameters so as to minimize the expected high-rate distortion. GMVQ systems produce low complexity by operating M Gaussian codebooks in parallel (typically with low-complexity structures) and then choosing amongst their outputs with an M-point vector quantizer. Thus, the total codebook is the union of the component Gaussian codebooks, and the total encoder regions are optimal provided the component encoders are optimal with respect to their individual codebooks