Publications – Details

 

Logarithmic Cubic Vector Quantization: Concept and Analysis

Authors:

Christian Rohlfing, Hauke Krüger, and Peter Vary

Book Title:

Proceedings of International Symposium on Information Theory and its Applications (ISITA)

Venue:

Honolulu, Hawai'i, USA

Event Date:

28.-31.10.2012

Organization:

IEICE

Date:

Oct. 2012

Pages:

294–298

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 of Publication

Copyright Notice

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.

The following notice applies to all IEEE publications:
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

File