On the block size of trellis quantizers
(2005) Proceedings. DCC 2005. Data Compression Conference In Proceedings. DCC 2005. Data Compression Conference p.457457 Abstract
 Summary form only given. In this paper, we examine the effect of block size on the performance of trellis based quantization. In particular, the Viterbi and tailbiting BCJR algorithms are compared. It is shown that for short blocks of data, the TBCJR algorithm achieves a superior performance over the Viterbi algorithm (VA). One approach is to use the maximum a posteriori probability (MAP) heuristic and the TBCJR algorithm. If the MAPencoder does not produce a tailbiting state sequence, the path is modified for a number of stages at the beginning and end of the block such that it tailbites. The enclosed figure shows MSE as a function of block size and sample position, respectively, for a rate R=1 bit per sample, 32state trellis... (More)
 Summary form only given. In this paper, we examine the effect of block size on the performance of trellis based quantization. In particular, the Viterbi and tailbiting BCJR algorithms are compared. It is shown that for short blocks of data, the TBCJR algorithm achieves a superior performance over the Viterbi algorithm (VA). One approach is to use the maximum a posteriori probability (MAP) heuristic and the TBCJR algorithm. If the MAPencoder does not produce a tailbiting state sequence, the path is modified for a number of stages at the beginning and end of the block such that it tailbites. The enclosed figure shows MSE as a function of block size and sample position, respectively, for a rate R=1 bit per sample, 32state trellis quantizer and an IID Gaussian source. The effects of startup are clearly visible. For the TBCJR algorithm, the distortion is evenly distributed across the whole block. The performance decrease for short blocks stems from the increase in the number of tailbiting violations for short blocks and the suboptimal modification to ensure tailbiting. The results presented here hold for a large class of trellis constructions, such as TCQ (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/615929
 author
 Eriksson, Tomas ^{LU} ; Hellerbrand, S; Anderson, John B ^{LU} and Novak, Mirek ^{LU}
 organization
 publishing date
 2005
 type
 Chapter in Book/Report/Conference proceeding
 publication status
 published
 subject
 keywords
 trellis based quantization, tailbiting BCJR algorithm, Viterbi algorithm, trellis quantizer block size effects, TBCJR, MAPencoder, maximum a posteriori probability heuristic, tailbiting state sequence, MSE, IID Gaussian source, startup distortion effects, TCQ, sample position effects, tailbiting violations
 in
 Proceedings. DCC 2005. Data Compression Conference
 pages
 457  457
 publisher
 IEEEInstitute of Electrical and Electronics Engineers Inc.
 conference name
 Proceedings. DCC 2005. Data Compression Conference
 external identifiers

 wos:000229070000054
 scopus:26944440089
 ISBN
 0769523099
 DOI
 10.1109/DCC.2005.62
 language
 English
 LU publication?
 yes
 id
 71b15d30ebce411d916d95c50e25bd80 (old id 615929)
 date added to LUP
 20071125 10:12:18
 date last changed
 20180107 10:34:12
@inproceedings{71b15d30ebce411d916d95c50e25bd80, abstract = {Summary form only given. In this paper, we examine the effect of block size on the performance of trellis based quantization. In particular, the Viterbi and tailbiting BCJR algorithms are compared. It is shown that for short blocks of data, the TBCJR algorithm achieves a superior performance over the Viterbi algorithm (VA). One approach is to use the maximum a posteriori probability (MAP) heuristic and the TBCJR algorithm. If the MAPencoder does not produce a tailbiting state sequence, the path is modified for a number of stages at the beginning and end of the block such that it tailbites. The enclosed figure shows MSE as a function of block size and sample position, respectively, for a rate R=1 bit per sample, 32state trellis quantizer and an IID Gaussian source. The effects of startup are clearly visible. For the TBCJR algorithm, the distortion is evenly distributed across the whole block. The performance decrease for short blocks stems from the increase in the number of tailbiting violations for short blocks and the suboptimal modification to ensure tailbiting. The results presented here hold for a large class of trellis constructions, such as TCQ}, author = {Eriksson, Tomas and Hellerbrand, S and Anderson, John B and Novak, Mirek}, booktitle = {Proceedings. DCC 2005. Data Compression Conference}, isbn = {0769523099}, keyword = {trellis based quantization,tailbiting BCJR algorithm,Viterbi algorithm,trellis quantizer block size effects,TBCJR,MAPencoder,maximum a posteriori probability heuristic,tailbiting state sequence,MSE,IID Gaussian source,startup distortion effects,TCQ,sample position effects,tailbiting violations}, language = {eng}, pages = {457457}, publisher = {IEEEInstitute of Electrical and Electronics Engineers Inc.}, title = {On the block size of trellis quantizers}, url = {http://dx.doi.org/10.1109/DCC.2005.62}, year = {2005}, }