Trellis-based scalar-vector quantizer for memoryless sources

Author

Laroia, Rajiv ; Farvardin, Nariman

Author_Institution

AT&T Bell Labs., Murray Hill, NJ, USA

Volume

40

Issue

3

fYear

1994

fDate

5/1/1994 12:00:00 AM

Firstpage

860

Lastpage

870

Abstract

The paper describes a structured vector quantization approach for stationary memoryless sources that combines the scalar-vector quantizer (SVQ) ideas (Laroia and Farvardin, 1993) with trellis coded quantization (Marcellin and Fischer, 1990). The resulting quantizer is called the trellis-based scalar-vector quantizer (TB-SVQ). The SVQ structure allows the TB-SVQ to realize a large boundary gain while the underlying trellis code enables it to achieve a significant portion of the total granular gain. For large block-lengths and powerful (possibly complex) trellis codes the TB-SVQ can, in principle, achieve the rate-distortion bound. As indicated by the results obtained, even for reasonable block-lengths and relatively simple trellis codes, the TB-SVQ outperforms all other fixed-rate quantizers at reasonable complexity

Keywords

analogue-digital conversion; trellis codes; vector quantisation; TB-SVQ; block-lengths; boundary gain; memoryless sources; rate-distortion bound; total granular gain; trellis code; trellis coded quantization; trellis-based scalar-vector quantizer; Convolutional codes; Costs; Encoding; Entropy; Laplace equations; Noise level; Performance gain; Probability density function; Quantization; Rate-distortion;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.335896

Filename

335896