本論文在第一部分提出一套影像壓縮系統架構應用於靜態灰階影像壓縮。實現此一系統架構主要分為四階段：第一階段應用離散小波轉換將原始灰階影像轉換成小波係數，由於小波係數除了在低頻部分的係數分佈很廣且擁有較大的能量，大部分高頻係數能量非常小並分佈在原點附近狹小的範圍內。因此在第二階段，即可利用 KLT找出 Level 1 與Level 2 的最主要的成分來代表整個subband的係數，然後成為第三階段─向量量化的輸入。當我們將第三階段輸入向量的維度減少後，在相同的預設壓縮比之下，可以大幅減少神經網路訓練的時間。節省時間的同時，我們並沒有犧牲影像品質。在第三階段裡，吾人利用一個自成長型神經網路，即週期性活動力守恆網路(PVC) 應用於產生一近似最佳化的向量量化器。至於低頻部分的小波係數則完全由純量量化器 (DPCM) 來量化。最後，第四階段利用適應性算數編碼將第三階段之輸出編碼，以便傳輸或儲存。實驗結果顯示，此一影像壓縮系統的確可以達到在高壓縮比時仍保有相當不錯的影像品質。在本論文的第二部分中，我們提出一套彩色影像壓縮系統應用於靜態全彩影像壓縮。首先，利用 PVC 訓練出一相關於影像的有限(256)色盤。接著，我們經由2-way量化器把一張影像切割成許多區塊，並分類成高頻與低頻部分。而PVC量化器針對不同類別的區塊代表顏色個別量化並結合量化誤差修正技術(QEC)將量化誤差減少到最低。實驗結果顯示，此一結合PVC 神經網路之2-way彩色影像壓縮系統的確可以達到在高壓縮比時比其它彩色影像壓縮系統 較好的影像品質。

In the first part of this thesis, we propose a MICS scheme for image compression of monochrome images based on the discrete wavelet transform and PVC algorithm. Implementation of the MICS scheme involves four major steps. Firstly, apply discrete wavelet transform to obtain a set of biorthogonal subbands of input image. The original image is decomposed at different scales using a pyramidal algorithm. Secondly, use the Karhunen-Loeve transform (KLT) to project wavelet coefficients of some subbands onto fewer principal components. Thirdly, apply a self-creating algorithm, namely, the periodical vitality conservation (PVC) to quantize both the output of KLT and the remaining subbands. Finally, the adaptive arithmetic coding is employed to encode the outputs of PVC. Performance comparisons with the embedded wavelet hierarchical image coder (EZW) (Shapiro, 1993), multi-threshold wavelet coder (MTWC) (Wang and Huo, 1997) and Lazar's coder (Lazar et al., 1996) were conducted. All simulation results indicate that quality reconstructed images can be obtained by using the MICS scheme, even at very low bit rate.
The second part of this thesis focuses on color image compression. The proposed CICS scheme first utilizes PVC to design a limited color palette. Then, a 2-way quantization approach is employed to divide all the image blocks into two classes, namely low-frequency and high-frequency classes. The training vectors of two classes can be presented separately and concurrently to two PVC networks for quantization. Furthermore, each training vector is composed of some number of representative colors, depending to which class the block belongs. Finally, when incorporated with QEC, SAQ and the excellent quantization performance delivered by the PVC network, the 2-way approach is shown capable of minimizing the effect of quantization error induced by the high-frequency class. Experimental results have been shown to justy all the claims in this thesis.

CHAPTER 1 INTRODUCTION 2
1.1 MOTIVATION 2
1.2 OUTLINE 4
CHAPTER 2 MONOCHROME IMAGE COMPRESSION SYSTEM 5
2.1 BASICS AND SURVAYS 5
2.2 DISCRETE WAVELET TRANSFORM 10
2.2.1 Wavelet Decomposition 10
2.2.2 Statistical Distribution of the Wavelet Coefficients 15
2.3 KARHUNEN-LOEVE TRANSFORM 18
2.4 TRAINING ALGORITHM FOR CODEBOOK DESIGN 20
2.5 EXPERIMENTAL RESULTS 28
2.5.1 Using Different Wavelets 28
2.5.2 Comparison with Other Coders 28
CHAPTER 3 COLOR IMAGE COMPRESSION SYSTEM 35
3.1 BACKGROUNDS 35
3.2 LIMITED COLOR PALETTE DESIGN 41
3.2.1 Color Quantization Using PVC 42
3.2.2 The Fast Codebook Searching Algorithm 44
3.3 THE TWO-WAY QUANTIZER 46
3.4 VECTOR QUANTIZATION USING PVC ALGORITHM 49
3.4.1 The Representative Colors 49
3.4.2 Quantization Error Correction (QEC) 49
3.4.3 QEC with Successive Approximation Quantization 50
3.5 EXPERIMENTAL RESULTS 52
CHAPTER 4 DISSCUSIONS AND CONCLUSIONS 67
4.1 CONTRIBUTIONS 67
4.2 FUTURE WORKS 68
REFERENCES 69

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