臺灣博碩士論文加值系統

　　近年來由於網際網路以及數位相機的盛行，個人的影像資料不斷地增加是可預期的結果。根據近期的研究，人們發現到採用離散小波轉換discrete wavelet transform (DWT) [4]為基礎的壓縮技巧，其壓縮效率往往勝過以離散餘弦轉換discrete cosine transform (DCT)為基礎的壓縮技術。因此近年來許多以DWT為基礎的壓縮技巧不斷的被提出與發表。

　　在小波影像壓縮應用中，Significance linked connected component analysis (SLCCA) [6]是一種有效率的小波係數呈現和組織的策略。以小波轉換為基礎。首先，一張數位影像先由空間域的訊號轉換為頻率域的訊號。在轉換的過程中，小波係數將以影像金字塔的方式進行排列。隨後這些金字塔中的係數將會以一個特定的量化值進行量化。而量化值的選擇將會由預先設定的壓縮位元率以及影像還原品質所決定。在量化程序後，剩餘的係數（非零項係數）將會經過significance link和connected component 兩個程序進行組織以及整理。在最後，和一般的壓縮演算法相同，SLCCA 編碼系統也採用了一系列的無失真壓縮編碼技巧來增進整體的壓縮效率。

　　依照相關文獻和期刊上實驗結果的探討，與其他常見的小波編碼演算法相比，SLCCA具有相當優異的壓縮效能。然而在我們試著實際建立SLCCA壓縮編碼系統的過程中，卻發現到在目前的文獻中，對於SLCCA演算法的陳述和實作上所需要的相關技術細節都沒有完整的說明，在其中不是資料不完全，就是沒有描述。因此在相關資料缺乏的情形下，也連帶對於SLCCA其他相關領域的研究產生很大的影響。

　　因此基於以上的原因，在本論文中，我們除了將詳細探討與說明SLCCA編碼系統的理論基礎以及運作原理，進而完整的建立SLCCA編碼系統的關鍵技術。另一方面，由於SLCCA演算法所產生出的資料序列間具有相當程度機率分佈的差異性。因此利用這些資料序列間不同的機率分佈特性，我們將提出幾種相關的壓縮機制，來進一步增加整體的影像壓縮效率。

　　Popularization of Internet and digital camera, the increase of the personal image data is an anticipated result. In recent years, many researchers have found that image compression techniques adopting discrete wavelet transform (DWT) [4] frequently outperform techniques adopting discrete cosine transform (DCT). So a lot of compression techniques involved DWT have been proposed recently.

　　For wavelet image compression coding, Significance linked connected component analysis (SLCCA) [6] is an efficient strategy for wavelet coefficients organization and representation. SLCCA coding system is based on wavelet transform. First, a digital image is transformed from time domain signal to frequency domain signal. During transforming process, the wavelet decomposition pyramid is constructed. Then, the wavelet coefficients in the pyramid are quantized by a uniform scalar quantizer. The quantizer is chosen by that the target bit rate and quality are satisfied. After quantization process, the remainder coefficients (nonzero coefficients) are organized and arranged by significance link process and connected component process. As in most image compression algorithms, the last step of SLCCA coding system involves entropy coding to compress further.

　　According to the literatures and papers of SLCCA, the performance of SLCCA is better than several other wavelet coders. But after studying these literatures about SLCCA, we find that many details of programming algorithm and information about implementation have not been mentioned in these literatures of SLCCA.

　　For this reason, we discuss and explain the principle and theory of SLCCA in this thesis. Further, we construct the know-how of SLCCA coding system completely. On the other hand, these characters of data strings generated by SLCCA are very different from one to another. So according to differences of data characters of strings, we propose some new techniques to improve the compression performance further.

Abstract iv
Table of contents vi
List of Figures viii
List of Tables x

Chapter

1 Introduction 1
1.1 Background 1
1.2 Motivation 5
1.3 An overview of the thesis 7

2 Discrete wavelet transformation 10
2.1 Introduction of wavelet 10
2.2 Implementation of 9/7 wavelet filter 14
2.3 Comparison with other wavelets 17

3 Quantization 23
3.1 Conception of uniform scalar quantizer 23
3.2 Quantization of SLCCA 26

4 Significance Linked Connected Component Analysis 29
4.1 Connected component 29
4.2 Significance link 36
4.3 Description of SLCCA algorithm 39

5 Data organization and representation strategy 48
5.1 Output strings of SLCCA 48
5.2 Compression of symbol string 51
5.3 Compression of magnitudes of significant
coefficients 59

6 Experimental results and analyses 61
6.1 Image set of performance evaluation 61
6.2 Comparison with other compression techniques 63

7 Conclusions and future works 67
7.1 Conclusion 67
7.2 Future works 68

Reference 69

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