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Sparse DP Quantization Algorithm
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Summary :
We formulate the design of an optimal quantizer as an optimization problem that finds the quantization indices that minimize quantization error. As a solution of the optimization problem, an approach based on dynamic programming, which is called DP quantization, is proposed. It is observed that quantized signals do not always contain all kinds of signal values which can be represented with given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to quantizer design. Signal values with zero frequency do not impact quantization error, so there is the potential to reduce the complexity of the optimal quantizer by not computing signal values that have zero frequency. However, conventional methods for DP quantization were not designed to consider amplitude sparseness, and so fail to reduce complexity. The proposed algorithm offers a reduced complexity optimal quantizer that minimizes quantization error while addressing amplitude sparseness. Experimental results show that the proposed algorithm can achieve complexity reduction over conventional DP quantization by 82.9 to 84.2% on average.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.3 pp.553-565
- Publication Date
- 2019/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.553
- Type of Manuscript
- PAPER
- Category
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Authors
Yukihiro BANDOH
NTT Corporation
Seishi TAKAMURA
NTT Corporation
Atsushi SHIMIZU
NTT Corporation
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Yukihiro BANDOH, Seishi TAKAMURA, Atsushi SHIMIZU, "Sparse DP Quantization Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 3, pp. 553-565, March 2019, doi: 10.1587/transfun.E102.A.553.
Abstract: We formulate the design of an optimal quantizer as an optimization problem that finds the quantization indices that minimize quantization error. As a solution of the optimization problem, an approach based on dynamic programming, which is called DP quantization, is proposed. It is observed that quantized signals do not always contain all kinds of signal values which can be represented with given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to quantizer design. Signal values with zero frequency do not impact quantization error, so there is the potential to reduce the complexity of the optimal quantizer by not computing signal values that have zero frequency. However, conventional methods for DP quantization were not designed to consider amplitude sparseness, and so fail to reduce complexity. The proposed algorithm offers a reduced complexity optimal quantizer that minimizes quantization error while addressing amplitude sparseness. Experimental results show that the proposed algorithm can achieve complexity reduction over conventional DP quantization by 82.9 to 84.2% on average.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.553/_p
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@ARTICLE{e102-a_3_553,
author={Yukihiro BANDOH, Seishi TAKAMURA, Atsushi SHIMIZU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sparse DP Quantization Algorithm},
year={2019},
volume={E102-A},
number={3},
pages={553-565},
abstract={We formulate the design of an optimal quantizer as an optimization problem that finds the quantization indices that minimize quantization error. As a solution of the optimization problem, an approach based on dynamic programming, which is called DP quantization, is proposed. It is observed that quantized signals do not always contain all kinds of signal values which can be represented with given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to quantizer design. Signal values with zero frequency do not impact quantization error, so there is the potential to reduce the complexity of the optimal quantizer by not computing signal values that have zero frequency. However, conventional methods for DP quantization were not designed to consider amplitude sparseness, and so fail to reduce complexity. The proposed algorithm offers a reduced complexity optimal quantizer that minimizes quantization error while addressing amplitude sparseness. Experimental results show that the proposed algorithm can achieve complexity reduction over conventional DP quantization by 82.9 to 84.2% on average.},
keywords={},
doi={10.1587/transfun.E102.A.553},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - Sparse DP Quantization Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 553
EP - 565
AU - Yukihiro BANDOH
AU - Seishi TAKAMURA
AU - Atsushi SHIMIZU
PY - 2019
DO - 10.1587/transfun.E102.A.553
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2019
AB - We formulate the design of an optimal quantizer as an optimization problem that finds the quantization indices that minimize quantization error. As a solution of the optimization problem, an approach based on dynamic programming, which is called DP quantization, is proposed. It is observed that quantized signals do not always contain all kinds of signal values which can be represented with given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to quantizer design. Signal values with zero frequency do not impact quantization error, so there is the potential to reduce the complexity of the optimal quantizer by not computing signal values that have zero frequency. However, conventional methods for DP quantization were not designed to consider amplitude sparseness, and so fail to reduce complexity. The proposed algorithm offers a reduced complexity optimal quantizer that minimizes quantization error while addressing amplitude sparseness. Experimental results show that the proposed algorithm can achieve complexity reduction over conventional DP quantization by 82.9 to 84.2% on average.
ER -
English
