Abstract
Classified vector quantization (VQ) using a Gaussian Mixture Model (GMM) preclassifier is a powerful VQ method. A prediction-based lower-triangular transform (PLT) is proposed for the enhancement of VQ in each cluster. The PLT is defined for generic vector spaces in the context of the covariance method of linear prediction (LP). Optimal quantizer banks are designed in minimum noise structures whose codebooks are used for the proposed Cartesian split VQ (CSVQ), which improves their coding gain. CSVQ is tested forline spectral frequency (LSF) quantization of wideband speech spectra, revealing a comparable average performance to the Karhunen-Loeve transform (KLT) at lower rates with reduced outlier generation and computational complexity.
Highlights
V ECTOR quantization (VQ) is more efficient than scalar quantization (SQ) but generally its search complexity is much higher and grows exponentially with dimension when full search is applied [1]
We have considered mainly nonuniform block scalar quantization (BSQ) with binary search, whose computational complexity is
The transform quantization methods discussed and proposed have been applied to sequences of line spectral frequency (LSF) vectors extracted from wideband speech signals
Summary
V ECTOR quantization (VQ) is more efficient than scalar quantization (SQ) but generally its search complexity is much higher and grows exponentially with dimension when full search is applied [1]. A successful approach factors the space into a Cartesian product of lower-dimensional subspaces in what is known as split VQ (SVQ) If the source vectors can be rendered completely independent, the scalar quantization of the components of the transformed vector is very efficient and flexible [5], even though vector quantization still holds the space-filling advantage [6]. Such an optimal transform is the source-specific Karhunen-Loeve transform (KLT) for jointly Gaussian sources.
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