Publications-Detail

Logarithmic Cubic Vector Quantization: Concept and Analysis

Authors:
Rohlfing, C. ,  Krüger, H.Vary, P.
Book Title:
Proceedings of International Symposium on Information Theory and its Applications (ISITA)
Organization:
IEICE
Pages:
p.p. 294-298
Date:
Oct. 2012
Language:
English

Abstract

In this paper, we analyze 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 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 based on the maximum norm (also denoted as L∞ -norm) instead of the typically used Euclidean norm (L2 -norm). Therefore, 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 yielding low computational complexity as well as high memory efficiency even in case of very high vector dimensions.

In this paper, the concept of LCVQ is presented. Also, theoretical quantization performance measures for LCVQ as well as the optimal allocation of bit rate for gain factor and shape vector are derived. In order to assess the proposed LCVQ approach, the quantization performance achieved by LCVQ is compared to results which were recently derived for Logarithmic Spherical Vector Quantization (LSVQ), another highly efficient GSVQ scheme proposed in [1].

Download

BibTeX

Copyright © by IKS
rohlfing12b.pdf
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.