DocumentCode :
1205658
Title :
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
Link To Document :
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