Analysis of Adaptive PCM with One Word Memory
Summary :
We consider an adaptive PCM system, in which the input samples are expanded or compressed by a constant factor c or 1/c each time before quantization. Assuming a stationary Gaussian input with a rational power spectral density, we derive an integral equation for the joint distribution of the input and the state of the system. Its solution provides us with a feasible way to numerical computation. The mean-squared error are computed for the Gauss-Markov input in terms of the constant c, the sampling interval T, bound parameters for the scaler and the number of quantization levels. The numerical results show good performance in comparison with regular PCM.
- Publication
- IEICE TRANSACTIONS on transactions Vol.E62-E No.6 pp.375-381
- Publication Date
- 1979/06/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Communication Theory
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Akira HAYASHI, "Analysis of Adaptive PCM with One Word Memory" in IEICE TRANSACTIONS on transactions,
vol. E62-E, no. 6, pp. 375-381, June 1979, doi: .
Abstract: We consider an adaptive PCM system, in which the input samples are expanded or compressed by a constant factor c or 1/c each time before quantization. Assuming a stationary Gaussian input with a rational power spectral density, we derive an integral equation for the joint distribution of the input and the state of the system. Its solution provides us with a feasible way to numerical computation. The mean-squared error are computed for the Gauss-Markov input in terms of the constant c, the sampling interval T, bound parameters for the scaler and the number of quantization levels. The numerical results show good performance in comparison with regular PCM.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e62-e_6_375/_p
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@ARTICLE{e62-e_6_375,
author={Akira HAYASHI, },
journal={IEICE TRANSACTIONS on transactions},
title={Analysis of Adaptive PCM with One Word Memory},
year={1979},
volume={E62-E},
number={6},
pages={375-381},
abstract={We consider an adaptive PCM system, in which the input samples are expanded or compressed by a constant factor c or 1/c each time before quantization. Assuming a stationary Gaussian input with a rational power spectral density, we derive an integral equation for the joint distribution of the input and the state of the system. Its solution provides us with a feasible way to numerical computation. The mean-squared error are computed for the Gauss-Markov input in terms of the constant c, the sampling interval T, bound parameters for the scaler and the number of quantization levels. The numerical results show good performance in comparison with regular PCM.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - Analysis of Adaptive PCM with One Word Memory
T2 - IEICE TRANSACTIONS on transactions
SP - 375
EP - 381
AU - Akira HAYASHI
PY - 1979
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E62-E
IS - 6
JA - IEICE TRANSACTIONS on transactions
Y1 - June 1979
AB - We consider an adaptive PCM system, in which the input samples are expanded or compressed by a constant factor c or 1/c each time before quantization. Assuming a stationary Gaussian input with a rational power spectral density, we derive an integral equation for the joint distribution of the input and the state of the system. Its solution provides us with a feasible way to numerical computation. The mean-squared error are computed for the Gauss-Markov input in terms of the constant c, the sampling interval T, bound parameters for the scaler and the number of quantization levels. The numerical results show good performance in comparison with regular PCM.
ER -
English
