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研究生:黃維聖
研究生(外文):Owen Huang
論文名稱:通道匹配之向量量化研究
論文名稱(外文):A Study on Channel-Matched Vector Quantization
指導教授:張文輝
指導教授(外文):Wen-Whei Chang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電信工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:中文
論文頁數:54
中文關鍵詞:向量量化通道模型雜訊通道向量量化
外文關鍵詞:Vector QuantizationChannel ModelNoise Channel Vector Quantization
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向量量化是一項重要的影音資料壓縮技術,但是碼書訓練演算法必須經過修正才能達到對抗通道雜訊的目的。其成敗關鍵在於是否能找到一個適當的機率模型來模擬傳輸通道的特性。在本論文中,首先介紹向量量化演算法及線性預測編碼頻譜的量化流程。目前雜訊通道向量量化的相關研究,只有考慮無記憶性通道模型,而這並不符合數位無線通訊的叢發性錯誤特性。有鑑於此,我們提出了針對記憶性通道特性設計的向量量化演算法,並將討論通道模型匹配的重要性。最後,我們也將討論空分割對於向量量化的影響及其建議處理方式。

Vector Quantization (VQ) has been wildly used in speech and image coding for data compression. It operates by encoding a sequence of input vectors with a codebook and by transmitting the index of the nearest codevector to the receiver. Thus, the effects of channel errors on transmitted codevector indices can result in significant distortion in decoded output. This provides the basic motivation for trying to reduce the channel distortion by generating suitable VQ codevectors in the training phase. Current research on channel matched VQ focus on memoryless binary symmetric channels. Unfortunately, however, transmission errors encountered in digital communication channel exhibits various degrees of statistical dependencies that are contigent on the transmission medium and on the particular modulation and demodulation technique used. Simulation results indicates that with the aid of Gilbert's channel the VQ training algorithm can be developed to better track the intrinsic natures of channel error.

第一章 緒論1
1.1研究動機及方向1
1.2章節概要3
第二章 線性預測編碼的頻譜量化4
2.1線頻譜對5
2.2向量量化流程8
2.3線頻譜對的向量量化13
第三章 雜訊通道向量量化16
3.1雜訊通道的向量量化17
3.2通道機率模型21
3.2.1無記憶性二元對稱通道模型21
3.2.2隱藏式碼可夫模型22
第四章 系統模擬27
4.1自回歸訊號源28
4.1.1無記憶性雜訊通道28
4.1.2記憶性雜訊通道35
4.1.3通道機率模型不匹配40
4.2線頻譜對43
4.3結論49
第五章 結論50
Reference52
Appendix54

[1] YOSEPH LINDE, ANDRES BUZO and ROBERT M. GRAY, An Algorithm for Vector Quantizer Design., IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. COM-28, NO. 1, JANUARY 1980.
[2] NARIMAN FARVARDIN, A Study of Vector Quantization for Noisy Channel, IEEE TRANSACTION ON INFORMATION THEORY, VOL. 36, NO. 4, JULY 1990
[3] NARIMAN FARVARDIN and VINAY VAISHAMPAYAN, On the Performance and Complexity of Channel-Optimized Vector Quantizers, IEEE TRANSACTIONS ON INFORMATION THEORY. VOL. 37, NO. 1, JANUARY 1991.
[4] Speech Coding and Synthesis, ELSEVIER 1995
[5] F. ITAKURA, Line Spectrum Representation of Linear Predictive Coefficients of Speech Signals, J. Acoust. Soc. Amer.. VOL. 57, p. S35, Apr, 1975
[6] KULDIP K. PALIWAL and BISHNU S. ATAL, Efficient Vector Quantization of LPC Parameters at 24 Bits/Frame, IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 1, JANUARY 1993.
[7] HAI LE VU and LASZLO LOIS, A New General Distance Measure for Quantization of LSF and Their Transformed Coefficients,
[8] E. N. GILBERT, Capacity of a Burst-Noise Channel, The Bell SystemTechnical Journal, 39, pp. 1253-1265, 1960
[9] JEAN-YVES CHOUINARD, MICHEL LECOURS and GILLES Y. DELISLE, Estimation of Gilbert's and Fritchman's Models Parameters Using the Gradient Method for Digital Mobile Radio Channels, IEEE TRANSACTION ON VEHICULAR TECHNOLOGY, VOL. 37, NO. 3, AUGUAT 1988
[10] JOHN G. PROAKIS, DIGITAL COMMUNICATIONS, THIRD EDITION, McGRAW-HILL
[11] A. M. KONDOZ, Digital Speech Coding for Low Bit Rate Communications Systems,WILEY.
[12] SHRINIVAS GADKARI and KENNETH ROSE, Noisy Channel Relaxation for VQ Design, IEEE TRANS. ON., 1996,2048-2051
[13] FRANK K. SOONG and BIING-HWANG JUANG, Optimal Quantization of LSP Parameters, IEEE TRANS. ON.,1988, 394-397

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