Paper Info
Title
Optimization of Parametric Companding Function for an Efficient Coding
Abstract
Designing a lossy source code remains one of the important topics in information theory, and has a lot of applications. Although plain vector quantization (VQ) can realize any fixed-length lossy source coding, it has a serious drawback in the computation cost. Companding vector quantization (CVQ) reduces the complexity by replacing vector quantization with a set of scalar quantizations. It can represent a wide class of practical VQs, while the structure in CVQ restricts it from representing every lossy source coding. In this article, we propose an optimization method for parametrized CVQ by utilizing a newly derived distortion formula. To test its validity, we applied the method especially to transform coding. We found that our trained CVQ outperforms Karhunen-Loëve transformation (KLT)-based coding not only in the case of linear mixtures of uniform sources, but also in the case of low bit-rate coding of a Gaussian source.
Year
DOI
Venue
2007
10.1007/978-3-540-69158-7_74
ICONIP (1)
Keywords
Field
DocType
plain vector quantization,gaussian source,fixed-length lossy source coding,parametric companding function,scalar quantization,efficient coding,companding vector quantization,trained cvq,parametrized cvq,lossy source code,uniform source,lossy source coding,source code,information theory,transform coding
Information theory,Lossy compression,Pattern recognition,Computer science,Transform coding,Companding,Vector quantization,Artificial intelligence,Shannon–Fano coding,Quantization (signal processing),Variable-length code
Conference
Volume
ISSN
Citations 
4984
0302-9743
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Shin-ichi Maeda1268.11
Shin Ishii223934.39