Logarithmic Cubic Vector Quantization
Christian Rohlfing, Hauke Krüger, Peter Vary · 2012
In this paper we present Logarithmic Cubic Vector Quantization (LCVQ), a novel type of gain-shape vector quantization (GSVQ). In LCVQ, the vector to be quantized is decomposed into a gain factor and a shape vec- tor which is a normalized version of the input vector. Both components are quantized independently and transmitted to the decoder. Compared to other GSVQ approaches, in LCVQ the input vectors are normalized such that all shape vectors are lo- cated on the surface of the unit hypercube. As a conclusion, the shape vector quantizer can be realized based on uniform scalar quantizers. This yields low computational complexi ty as well as high memory efficiency even in case of very high vector dimensions. In order to demonstrate the coding efficiency of the proposed quantization scheme, LCVQ is compared to existing quanti- zation schemes, the recently proposed logarithmic spheric al vector quantization (LSVQ), logarithmic scalar quantizat ion (LSQ) and adaptive quantization backward (AQB).