通道匹配之向量量化研究

Abstract

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.