Rohlfing, C.; Krüger, H.; Vary, P. (Institute of Communication Systems and Data Processing, RWTH Aachen University, 52074 Aachen, Germany)
In this paper a novel type of gain-shape vector quantization (GSVQ) is presented, denoted as Logarithmic Cubic Vector Quantization (LCVQ). LCVQ is based on a decomposition of the vector to be quantized into a gain factor and a shape vector 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 located 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 complexity 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 quantization schemes, in particular the recently proposed Logarithmic Spherical Vector Quantization (LSVQ) .