隨著多媒體資訊的快速增長，視頻編碼標準在傳遞大量的視訊資料時，已經變得非常重要。運動估計藉由去除視訊資料中的時間冗餘物，絕對是高性能視頻編碼的關鍵。視頻轉碼，也成為一個適用於不同頻寬變換的重要方法。因此，在運動估計和視頻轉編碼的研究工作目前已被廣泛展開。在此論文中，概述了視頻壓縮技術，並將重點放在運動估計方法上。然後，介紹最具有代表性的運動估計搜尋演算法和提出的運動估計演算法，並實作出一些著名的視頻序列的實驗，評價和分析這些演算法。除此之外，也提出一個基於視覺注意力模型的有效視頻轉編碼，它使用拉格朗日最佳化來得到最低的失真成本。最後，對未來趨勢視頻編碼進行討論。透過提出的運動估計演算法，計算複雜度可以大大的降低，而在客觀的表現上卻只有些許的衰退。另外，所提出的視頻轉編碼方法，可以有效的降低位元率以符合所要求的頻寬。

With the rapid growth of multimedia information, video coding standards have become crucial when transmitting large amount of video data. Motion estimation promises to be the key to high performance in video coding by removing the temporal redundancy of video data for storage and transmission. Video transcoding also becomes a significant scheme applied in different bandwidth transform. Due to their fundamentality, research works on motion estimation and video transcoding have been conducted extensively. In this thesis, an overview of video compression technique is presented with emphasis on motion estimation. Then, a survey of most representative motion estimation search algorithms and the proposed motion estimation algorithms are introduced. The evaluation and analysis of these algorithms based on a number of experiments on several famous test video sequences is presented. In addition, an efficient video transcoding via visual attention model with Lagrange optimization to minimum rate-distortion cost is proposed. Finally, an investigation of the future trend of video coding is discussed. Through the proposed algorithms of motion estimation, the computational complexity can be significantly reduced despite the fact that the objective quality of motion compensated images is slightly degraded. Moreover, through the proposed video transcoding method, the bit rate can be reduced to fit the requirement of bandwidth.

CHAPTER 1 INTRODUCTION 1
1.1 Overview of Video Coding and Compression 1
1.2 Overview of Motion Estimation and Compensation 3
1.3 Overview of Video Transcoding 11
1.4 Motivation 12
1.5 Organization of the Thesis 14
CHAPTER 2 Related Work 16
2.1 Related Works of Motion Estimation 16
2.1.1 Search-point Reduction Method…………………………………..…17
2.1.1.1 Full Search………………………………………..…………....17
2.1.1.2 Three Step Search…………..……………………………..…...17
2.1.1.3 New Three Step Search……………..…………….…………...19
2.1.1.4 Four Step Search………………..……………………….…….20
2.1.1.5 Block-Based Gradient Descent Search…………..……..……...22
2.1.1.6 Diamond Search………………..………………….…………..23
2.1.1.7 Hexagon-based Search……………………..………...………..25
2.1.1.8 Quarter Pixel Interpolation Search………….…………………27
2.1.1.9 New Cellular Search………………..………………………….28
2.1.2 Calculation Reduction Method……………………….……………...31
2.1.2.1 Normalized Partial Distortion Search……………………….....31
2.1.2.2 Neighboring Block-Based Search………..……...…………….33
2.2 Discussion 35
2.2.1 Comparisons of Motion Estimation Algorithms………...…………..35
2.2.2 Future Trend of Video Coding………..…………………………….36
2.3 Experimental Results 39
2.4 Summary 40
CHAPTER 3 Coarse-to-Fine Normalized Partial Distortion Search and Successive Accumulating Partial Distortion Search Algorithms 45
3.1 Coarse-to-Fine Normalized Partial Distortion Search Algorithm… 45
3.1.1 Coarse-to-Fine Normalized Partial Distortion Search Algorithm… 45
3.1.2 Experimental Results 52
3.1.3 Summary 56
3.2 Successive Accumulating Partial Distortion Search Algorithm .. ……………………………………………………………………...57
3.2.1 Successive Accumulating Partial Distortion Search Algorithm…..……………………………………..………………...58
3.2.2 Experimental Results 64
3.2.3 Summary 66
CHAPTER 4 Correlation-Based Normalized Partial Distortion Search Algorithm 69
4.1 Correlation-Based Normalized Partial Distortion Search Algorithm… 71
4.2 Experimental Results 77
4.3 Summary 79
CHAPTER 5 Video Transcoding via Visual Attention Model with Lagrange Optimization 82
5.1 Related Works… 83
5.2 Video Transcoding via Visual Attention Model with Lagrange Optimization… 84
5.2.1 Visual Attention Model 85
5.2.1.1 Color Quantization 86
5.2.1.2 Color Space Transformation 87
5.2.1.3 Contrast Value Calculation 89
5.2.2 Visual Attention Region Extraction 90
5.2.3 Video Transcoding via Visual Attention Model with Lagrange Optimization 91
5.3 Experimental Results 93
5.4 Summary 96
CHAPTER 6 Conclusions and Future Work 98
BIBLIOGRAPHY……………………………………………………………………101

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