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A Method for Optimization issue of Quasi-logarithmic Quantizers

Zoran Peric, Aleksandra Jovanovic, Milan Tancic
Faculty of Electronic Engineering at University of NiÃ…Â¡, Aleksandra Medvedeva 14 & NiÃ…Â¡ 18000, Serbia

Abstract: This work has addressed the optimization issue of quasi-logarithmic quantizer for memoryless Laplacian source. An iterative method for determination of compression factor ÃŽÂ¼ and support limit xmax that gives the highest signal-to-quantization noise ratio SQNR is advocated. Further we have explained the optimization procedure. The experimental results support the suitability and success of this model.

Keywords: Companding Quantizer, Quasi-logarithmic Compression Function, Laplacian Probability Density Function, Iterative Optimization Method A Method for Optimization issue of Quasi-logarithmic Quantizers

DOI:https://doi.org/10.6025/jdp/2021/11/2/35-40

Full_Text PDF 460 KB Download: 180 times

References:

[1] Jayant, N. S., Noll, P. (1984). Digital Coding of Waveforms, Principles and Applications to Speech and Video, 2nd ed., New Jersey: Prentice Hall, 1984, ch. 4, p. 115-220.
[2] Nikoli, J., Peri, Z., Aleksi, D. (2013). Optimization of μ- law companding quantizer for Laplacian source using Muller’s method, Przeglad Elektrotevhniczny, 89 (3A) 206- 208.
[3] Na, S., Neuhoff, D. L. (2001). On the support of MSE-optimal, fixed-rate, scalar quantizers, IEEE Trans. Inf. Theory, vol. 47 (7) 2972–2982, (November).
[4] Na, S., Neuhoff, D. L. (2012). Asymptotic MSE distortion of mismatched uniform scalar quantization, IEEE Trans. Inf. Theory, 58 (5) 3169–3181, (May).
[5] Aleksi, D., Peri, Z., Nikoli, J. (2012). Support region determination of quasi-logarithmic quanitzer for Laplacian source, Przeglad Elektrotevhniczny, 88 (7A) 130-132.
[6] Peric, Z., Aleksic, D., Stefanovic, M., Nikoli, J. (2013). New approach to support region determination of the μ-law quantizer, ELEKTRONIKA IR ELEKTROTECHNIKA, 19 (8) 111-114.

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