Embedded Voronoi codes for successive refinement lattice vector quantization

Abstract

Lattice Vector Quantization (LVQ) is an interesting tool in source coding which can take advantage of a higher dimension than the scalar case while overcoming complexity limitations of conventional vector quantization. However, the high dimension and the relatively complex indexing of the codebooks make LVQ often unsuitable for getting a successive refinement of the source. For addressing this problem, the paper proposes a new class of LVQ called the embedded Voronoi codes. The new codes can gradually describe the source with a granularity of 1 bit/dimension by properly combining differently scaled Voronoi codes. A rate-distortion evaluation for a Gaussian source shows that the embedding of the codes comes at a minimal cost at low bit-rates while preserving LVQ advantages over scalar quantization.