Title
An adaptive technique for accuracy enhancement of vector quantizers in nonorthogonal domains
Abbreviated Journal Title
IEEE Trans. Circuits Syst. I-Fundam. Theor. Appl.
Keywords
vector quantizers in nonorthogonal domains; IMAGE REPRESENTATION; SIGNAL REPRESENTATION; MIXED-TRANSFORMS; QUANTIZATION; SPEECH; COMPRESSION; ALGORITHMS; Engineering, Electrical & Electronic
Abstract
Recently, novel vector quantization techniques in multiple nonorthogonal domains for both waveform and model-based signal characterization that give an improved qualitative and quantitative signal coding performance as compared to vector quantization in single domain have been reported. In these techniques, vectors are formed either directly from the signal waveform or from the model parameters extracted from the signal. Then, the vectors are represented in multiple nonorthogonal. domains. The encoder chooses the domain that best represents the vector according to a predetermined criterion. An iterative codebook enhancement algorithm, applicable to both waveform and model-based vector quantization in nonorthogonal domains is developed and presented in this paper. In this algorithm, each set of codebooks in a given domain is retrained by the vectors that were best represented by that particular set of codebooks in the most recent iteration. The algorithm is applied successfully and extensive simulation results yield considerable performance enhancement of the vector quantization in nonorthogonal domains, for a given bit rate. Sample results are provided which demonstrate the improved performance.
Journal Title
Ieee Transactions on Circuits and Systems I-Fundamental Theory and Applications
Volume
50
Issue/Number
12
Publication Date
1-1-2003
Document Type
Article
Language
English
First Page
1548
Last Page
1555
WOS Identifier
ISSN
1057-7122
Recommended Citation
"An adaptive technique for accuracy enhancement of vector quantizers in nonorthogonal domains" (2003). Faculty Bibliography 2000s. 3869.
https://stars.library.ucf.edu/facultybib2000/3869
Comments
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