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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2013 - 13

Linearization of Optimal Compressor Function and Design of Piecewise Linear Compandor for Gaussian Source

NIKOLIC, J. See more information about NIKOLIC, J. on SCOPUS See more information about NIKOLIC, J. on IEEExplore See more information about NIKOLIC, J. on Web of Science, PERIC, Z. See more information about  PERIC, Z. on SCOPUS See more information about  PERIC, Z. on SCOPUS See more information about PERIC, Z. on Web of Science, ALEKSIC, D. See more information about  ALEKSIC, D. on SCOPUS See more information about  ALEKSIC, D. on SCOPUS See more information about ALEKSIC, D. on Web of Science, ANTIC, D. See more information about ANTIC, D. on SCOPUS See more information about ANTIC, D. on SCOPUS See more information about ANTIC, D. on Web of Science
 
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Download PDF pdficon (684 KB) | Citation | Downloads: 526 | Views: 2,473

Author keywords
distortion, piecewise linear approximation, quantization, signal to noise ratio

References keywords
quantization(8), nikolic(8), scalar(7), coding(6), adaptive(6), speech(5), quantizer(5), algorithm(5), uniform(4), theory(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2013-11-30
Volume 13, Issue 4, Year 2013, On page(s): 73 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.04013
Web of Science Accession Number: 000331461300013
SCOPUS ID: 84890215004

Abstract
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The constraints on the quantizer model are usually related to how complex the model can be designed and implemented. For the given bit rate, it is desirable to provide the highest possible signal to quantization noise ratio (SQNR) with reasonable complexity of a quantizer model. In order to avoid the influence of compressor function nonlinearity and the difficulties appearing in implementing and designing, especially in the Gaussian probability density function case, in this paper we linearize the optimal compressor function within the segments. We take advantage of piecewise linearization of the optimal compressor function, as a convenient solution for less complex designing compared to the asymptotically optimal compandor, and we provide performances close to the ones of the asymptotically optimal compandor. This makes our model useful in applications where the design and implementation complexity is a decisive factor. We propose a piecewise linear compandor (PLC) with an equal number of reproduction levels per nonuniformly spaced segments, where the segment thresholds are allotted to the equidistant optimal compressor function values. We study how the number of segments affects SQNR of the PLC. Features of the proposed PLC indicate its theoretical and practical significance in quantization of Gaussian source signals.


References | Cited By  «-- Click to see who has cited this paper

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[2] A. Gersho, R. M. Gray, Vector Quantization and Signal Compression. Norwell, Kluwer Academic Publishers, Chapter 5, pp. 133-202, 1992.

[3] W. C. Chu, Speech Coding Algorithms, Foundation and Evolution of Standardized Coders. John Wiley & Sons, New Jersey, Chapters 5-6, pp. 143-183, 2003.

[4] L. Hanzo, C. Somerville, J. Woodard, Voice and Audio Compression for Wireless Communications. London, John Wiley & Sons, Chapter 1, pp. 1-28, 2007.

[5] Z. Peric, J. Nikolic, "Design of piecewise uniform scalar quantizer with geometric progression of segment width," Advances in Electrical and Computer Engineering, vol. 11, pp. 37 40, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[6] L. Velimirovic, Z. Peric, J. Nikolic, "Design of novel piecewise uniform scalar quantizer for Gaussian memoryless source," Radio Science, vol. 47, RS2005, 2012.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[7] J. Nikolic, Z. Peric, A. Jovanovic, D. Antic, "Design of forward adaptive piecewise uniform scalar quantizer with optimized reproduction level distribution per segments," Electronics and Electrical Engineering, vol. 119, pp. 19 22, 2012.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6]


[8] A. Thompson, D. Emerson, F. Schwab, "Convenient formulas for quantization efficiency," Radio Science, vol. 42, RS3022, 2007.
[CrossRef] [Web of Science Times Cited 19]


[9] J. Max, "Quantization for minimum distortion," IEEE Trans. Inf. Theory, vol. 6, pp. 7 12, 1960.
[CrossRef] [SCOPUS Times Cited 1460]


[10] S. P. Lloyd, "Least squares quantization in PCM," IEEE Trans. Inf. Theory, vol. 28, pp. 129 137, 1982.
[CrossRef] [Web of Science Times Cited 6627] [SCOPUS Times Cited 8132]


[11] Z. Peric, J. Nikolic, "An effective method for initialization of Lloyd-Max’s algorithm of optimal scalar quantization for Laplacian source," Informatica, vol. 18, pp. 279-288, 2007. [Online] Available: Temporary on-line reference link removed - see the PDF document

[12] J. Nikolic, Z. Peric, "Lloyd-Max's algorithm implementation in speech coding algorithm based on forward adaptive technique," Informatica, vol. 19, pp. 255-270, 2008. [Online] Available: Temporary on-line reference link removed - see the PDF document

[13] Z. Peric, J. Nikolic,, "An adaptive waveform coding algorithm and its application in speech coding," Digital Signal Processing, vol. 22, pp. 199-209, 2012.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 24]


[14] Z. Peric, J. Nikolic, "High-quality Laplacian source quantisation using a combination of restricted and unrestricted logarithmic quantisers," IET Signal Processing, vol. 6, pp. 633 640, 2012.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 12]


[15] S. Na, D. L. Neuhoff, "Asymptotic MSE distortion of mismatched uniform scalar quantization," IEEE Trans. Inf. Theory, vol. 58, pp. 3169-3181, 2012.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 18]


[16] D. Kazakos, K. Makki, "Robust Companders," Proc. 6th International Conference on Telecommunications and Informatics, Dallas, Texas, pp. 32-35, 2007. [Online] Available: Temporary on-line reference link removed - see the PDF document

[17] J. Nikolic, Z. Peric, D. Antic, A. Jovanovic, D. Denic, "Low complex forward adaptive loss compression algorithm and its application in speech coding," Journal of Electrical Engineering, vol. 62, pp. 19-24, 2011.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 16]


[18] S. Na, D. L. Neuhoff, "On the support of MSE-optimal, fixed-rate, scalar quantizers," IEEE Trans. Inf. Theory, vol. 47, pp. 2972-2982, 2001.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 57]


[19] S. Na, "Asymptotic formulas for variance-mismatched fixed-rate scalar quantization of a Gaussian source," IEEE Trans. Signal Process., vol. 59, pp. 2437-2441, 2011.
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[21] V. Despotovic, Z. Peric, L. Velimirovic, V. Delic, "DPCM with forward gain-adaptive quantizer and switched first order predictor for high quality speech signals," Advances in Electrical and Computer Engineering, vol. 10, pp. 95-98, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]


[22] V. Despotovic, Z. Peric, "ADPCM Using a Second-order Switched Predictor and Adaptive Quantizer," Advances in Electrical and Computer Engineering, vol. 11, pp. 61-64, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]




References Weight

Web of Science® Citations for all references: 6,860 TCR
SCOPUS® Citations for all references: 9,816 TCR

Web of Science® Average Citations per reference: 298 ACR
SCOPUS® Average Citations per reference: 427 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-05-23 06:47 in 95 seconds.




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