甲蟲王

論文名稱	Constrained-storage multistage vector quantization based on genetic algorithm
發表日期	2008-12-01
論文收錄分類	SCI
所有作者	Shiueng Bien Yang
作者順序	第一作者
通訊作者	是
刊物名稱	pattern recognition
發表卷數	17
是否具有審稿制度	是
發表期數	4
期刊或學報出版地國別/地區	NATTWN-中華民國
發表年份	2008
發表月份	1
發表形式	電子期刊
所屬計劃案	無
可公開文檔
可公開文檔
可公開文檔
附件	PR2008.pdf

[摘要] :
Multistage vector quantization (MSVQ) and their variants have been recently proposed. Before MSVQ is designed, the user must artificially
determine the number of codewords in each VQ stage. However, the users usually have no idea regarding the number of codewords in each VQ
stage, and thus doubt whether the resulting MSVQ is optimal. This paper proposes the genetic design (GD) algorithm to design the MSVQ.
The GD algorithm can automatically find the number of codewords to optimize each VQ stage according to the rate–distortion performance.
Thus, the MSVQ based on the GD algorithm, namely MSVQ(GD), is proposed here. Furthermore, using a sharing codebook (SC) can further
reduce the storage size of MSVQ. Combining numerous similar codewords in the VQ stages of MSVQ produces the codewords of the sharing
codebook. This paper proposes the genetic merge (GM) algorithm to design the SC of MSVQ. Therefore, the constrained-storage MSVQ using
a SC, namely CSMSVQ, is proposed and outperforms other MSVQs in the experiments presented here.
 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

[英文摘要] :
Multistage vector quantization (MSVQ) and their variants have been recently proposed. Before MSVQ is designed, the user must artificially
determine the number of codewords in each VQ stage. However, the users usually have no idea regarding the number of codewords in each VQ
stage, and thus doubt whether the resulting MSVQ is optimal. This paper proposes the genetic design (GD) algorithm to design the MSVQ.
The GD algorithm can automatically find the number of codewords to optimize each VQ stage according to the rate–distortion performance.
Thus, the MSVQ based on the GD algorithm, namely MSVQ(GD), is proposed here. Furthermore, using a sharing codebook (SC) can further
reduce the storage size of MSVQ. Combining numerous similar codewords in the VQ stages of MSVQ produces the codewords of the sharing
codebook. This paper proposes the genetic merge (GM) algorithm to design the SC of MSVQ. Therefore, the constrained-storage MSVQ using
a SC, namely CSMSVQ, is proposed and outperforms other MSVQs in the experiments presented here.
 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

[參考文獻] :
[1] Y. Linde, A. Buzo, R.M. Gray, An algorithm for vector quantizer design,
IEEE Trans. Commun. 28 (1980) 84–95.
[2] A. Gersho, R.M. Gray, Vector Quantization and Signal Compression,
Kluwer, Boston, 1992.
[3] B.H. Juang, A.H. Gray, Multiple stage vector quantization for speech
coding, in: Proceedings of the IEEE International Conference on
Acoustics, Speech, Signal Processing, 1982, pp. 597–600.
[4] C.F. Barnes, R.L. Frost, Vector quantizers with direct sum codebooks,
IEEE Trans. Inf. Theory 39 (1993) 565–580.
[5] A.U. Khan, A.H. Mousa, Image coding using entropy-constrained
reflected residual vector quantization, in: Proceedings of the IEEE
International Conference on Image Processing, 2002, pp. 253–256.
[6] W.J. Hwang, B.Y.Ye, Storage- and entropy-constrained multi-stage vector
quantization and its application to progressive image transmission, IEEE
Trans. Consum. Electron. 43 (1) (1997) 17–23.
[7] W.Y. Chan, A. Gersho, Enhanced multistage vector quantization with
constrained storage, in: Proceedings of the 24th Asilomar Conference
on Circuits, Systems, and Computers, 1990, pp. 659–663.
[8] W.Y. Chan, A. Gersho, High fidelity audio transform coding with vector
quantization, in: Proceedings of the IEEE International Conference on
Acoustics, Speech, Signal Processing, 1990, pp. 1109–1112.
[9] W.Y. Chan, A. Gersho, Constrained-storage quantization of multiple
vector sources by codebook sharing, IEEE Trans. Commun. 39 (1) (1991)
11–13.
[10] F. Kossentini, M.J.T. Smith, C.F. Barnes, Image coding using entropyconstrained
residual vector quantization, IEEE Trans. Image Process. 4
(10) (1995) 1349–1357.
[11] F. Kossentini, W.C. Chung, M.J.T. Smith, Conditional entropyconstrained
residual VQ with application to image coding, IEEE Trans.
Image Process. 5 (2) (1996) 311–320.
[12] F. Kossentini, J.T. Smith, A fast PNN design algorithm for entropyconstraint
residual vector quantization, IEEE Trans. Image Process. 7
(7) (1998) 1045–1050.
[13] S. Ramakrishnan, K. Rose, A. Gersho, Constraint-storage vector
quantization with a universal codebook, IEEE Trans. Image Process. 7
(6) (1998) 785–793.
[14] Y. Linde, A. Buzo, R.M. Gray, An algorithm for vector quantizer design,
IEEE Trans. Commun. 28 (1) (1980) 84–95.
[15] A. Kimura, T. Kawanishi, K. Kashino, Acceleration of similaritybased
partial image retrieval using multistage vector quantization, in:
Proceedings of the Pattern Recognition, 17th International Conference
on (ICPR’04), 2004, pp. 993–996.
[16] V. Krishnan, D.V. Anderson, K.K. Truong, Optimal multistage vector
quantization of LPC parameters over noisy channels, IEEE Trans. Speech
Audio Process. 12 (1) (2004) 1–8.
[17] N. Phamdo, N. Farvardin, T. Moriya,A unified approach to tree structured
and multistage vector quantization for noisy channels, IEEE Trans. Inf.
Theory 39 (1993) 835–850.
[18] Z.M. Lu, D.G. Xu, S.H. Sun, Multipurpose image watermarking
algorithm based on multistage vector quantization, IEEE Trans. Image
Process. 14 (6) (2005) 822–831.
[19] F. Lahouti, A.K. Khandani, A. Saleh, Robust transmission of
multistage vector quantized sources over noisy communication
channels—applications to MELP speech codec, IEEE Trans. Veh.
Technol. 55 (6) (2006) 1805–1811.
[20] S.Z. Selim, M.A. Ismail, K-means-type algorithm: generalized
convergence theorem and characterization of local optimality, IEEE
Trans. Pattern Anal. March. Intell. 6 (1984) 81–87.
[21] J.S. Pan, F.R. McInnes, M.A. Jack, VQ codebook design using genetic
algorithms, Electron. Lett. 31 (1995) 1418–1419.
[22] M. Begum, N. Nahar, K. Fatimah, M.K. Hasan, M.A. Rahman, An
efficient algorithm for codebook design in transform vector quantization,
in: Proceedings of the WSCG, 2003.
[23] H. Sun, K.Y. Lam, A.L. Chung, W. Dong, M. Gu, J. Sun, Efficient
vector quantization using genetic algorithm, Neural Comput. Appl. 14
(2005) 203–211.
[24] T.H. Cormen, C.E. Leiserson, R.L. Rivest, Introduction to Algorithms,
The MIT Press, Cambridge, MA, 1990.
[25] W.H. Equitz, A new vector quantization clustering algorithm, IEEE
Trans. Acoust. Speech Signal Process. (1989) 1568–1575.
[26] J.S. Garofolo, L.F. Lamel, W.M. Fisher, J.G. Fiscus, D.S. Pallett, N.L.
Dahlgren, The DARPA TIMIT Acoustic-Phonetic Continuous Speech
Corpus, 1993 [CD-ROM] NTIS PB91-100 354.