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研究生:劉家綺
研究生(外文):Chia-Chi Liu
論文名稱:基於DPCM與適應性量化的無失真/近無失真壓縮演算法
論文名稱(外文):A Lossless/Near-lossless Compression Algorithm Based on DPCM and Adaptive Quantization
指導教授:戴顯權戴顯權引用關係
指導教授(外文):Shen-Chuan Tai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
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:2004
畢業學年度:92
語文別:英文
論文頁數:50
中文關鍵詞:無失真近無失真壓縮影像
外文關鍵詞:near-losslessimagecompressionlossless
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  本論文所提出的演算法是對影像做無失真和近無失真的壓縮,在無失真壓縮部分,是以DPCM為基礎及提出的無失真壓縮演算法為核心。而近無失真壓縮的部分,則是依循無失真壓縮的作法再加上適應性量化來達到目標。提出的無失真演算法是針對影像中邊(edge)的特性分成5種模式來做預測,其中包含規律模式(regular mode)、水平邊緣模式(horizontal edge mode)、垂直邊緣模式(horizontal edge mode)、對角邊緣模式(diagonal edge mode)和無邊緣模式(non-edge mode)。模式的預測是利用相鄰像素的特性來判斷。此外,在適應性量化的設計上是考慮人眼對平滑區域的失真比複雜區域的失真敏感,所以針對這點,在量化部分使用了3個量化步階(Quantization step size)。實驗結果顯示,在無失真壓縮部分,雖然壓縮比並不如JPEG-LS,但計算複雜度和空間複雜度上皆比JPEG-LS低,且在近無失真壓縮部分,在視覺效果上亦比JPEG-LS的近無失真結果佳。
  This thesis is aim at lossless and near lossless image compression. The lossless compression part is based on DPCM, and it is the core of the near-lossless part. In the near lossless part, the adaptive quantization is used. The proposed lossless algorithm is designed in five modes which are the regular mode, the horizontal edge mode, the vertical edge mode, the diagonal edge mode and the non-edge mode. The mode decision is also needed to decide which mode is given by observing the behavior of the pixels around the predictive pixel. In terms of HVS, human eyes are more sensitive to smooth regions, that is, serious distortions existing in smooth regions is easier to see. Owing to this reason, in quantization part, three quantization step sizes are designed. Experimental results show that, in lossless part, although the compression ratio of the proposed algorithm is not as good as JPEG-LS, but the computational complexity is decreased by discarding the context modeling. In near-lossless part, the perceptual quality of the reconstructed images is also better than JPEG-LS.
LIST OF TABLES i
LIST OF FIGURES ii

CHAPTER 1 Introduction 1

CHAPTER 2 Introduction to JPEG-LS 3
2.1 Modeling and Prediction 4
2.2 Context Modeling 5
2.2.1 Parameterization 5
2.2.2 Error residual alphabet in JPEG-LS 7
2.2.3 Context determination 7
2.3 Adaptive correction 8
2.4 Coding 10
2.5 Embedded Alphabet Extension: Run Mode 11
2.6 Embedded Alphabet Extension: Run Mode 12

CHAPTER 3 The Proposed Lossless and Near-Lossless Algorithm 14
3.1 overview of the Proposed Algorithm 14
3.2 The Proposed Lossless Algorithm 16
3.2.1 The Estimation of the Direction of the Pixel 16
3.2.2 The Regular Mode 17
3.2.3 The Horizontal Edge Mode 18
3.2.4 The Vertical Edge Mode 19
3.2.5 The Diagonal Edge Mode 20
3.2.6 The Non-Edge Mode 23
3.3 The Entropy Coding 23
3.4 Summary of encoding procedures 25
3.5 Near-lossless compression 26
3.5.1 Prediction and Quantization 26
3.5.2 Entropy Coding in Near-Lossless Compression Coding 30
3.5.2.1 Golomb-Rice Code in Near-Lossless Coding 31
3.5.2.2 Huffman Code in Near-Lossless Coding 32

CHAPTER 4 Experimental Results 34
4.1 Assessment 34
4.2 Experimental Results of Lossless algorithm 34
4.3 Experimental Results of Near-Lossless algorithm 36
CHAPTER 5 Conclusions and Future Works 47

REFERENCE 48
BIOGRAPHY 50
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[14] R. Gallager and D. V. Voorhis, “Optimal source codes for geometrically distributed integer alphabets,” IEEE Trans. Inform. Theory, vol. IT-21, pp. 228–230, Mar. 1975.
[15] R. F. Rice, “Some practical universal noiseless coding techniques—Parts I–III,” Tech. Rep. JPL-79-22, Jet Propul. Lab., Pasadena, CA, Mar. 1979.
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