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研究生: 李昇家
Li, Sheng-Jia
論文名稱: 加速H.264運動向量估計模式的選擇
Speed-Up of the Selections of Motion-Estimation Modes in H.264
指導教授: 戴顯權
Tai, Shen-Chun
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2004
畢業學年度: 92
語文別: 英文
論文頁數: 48
中文關鍵詞: 運動向量估計多尺寸方塊
外文關鍵詞: motion estimation, variable block size
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  •   對於多尺寸方塊的運動向量估計,本論文提出一個有效率的以合併為基礎的演算法,用來降低H.264視訊編碼系統中運動向量估計步驟的計算複雜度。 首先對44尺寸的小方塊用完全搜尋的方式作整數像素精確度的運動向量估計,並且為每一個44小方塊保留一些運動向量做候補。這些候補的運動向量被用來決定相鄰方塊是否可以合併成為H.264所定義的其他形狀的方塊。因為只對44小方塊作運動向量估計的程序,所以其它尺寸方塊的運動測量計算負載將被節省下來。對於在外部 (inter)和內部 (intra)模式之間資料量-失真間 (RD)的最佳模式選擇,門檻值(threshold)決策方法將被採用以節省外部 (inter)- 168, 816, 88模式和內部模式的計算負載。模擬結果顯示出此簡單演算法的效能接近於 - 當1616, 168, 816, 88, 84, 48和44尺寸方塊皆被啟用時,H.264視訊編碼系統的測試程式JM7.3,而計算複雜度則明顯地降低。

      This Thesis presents an efficient merge-based algorithm for variable-size block matching motion estimation to reduce the computation load of motion estimation process in H.264 video encoder. Initially, fixed-size 44 small blocks are used for the integer-pixel accuracy full-search motion estimation and several candidate motion vectors are reserved for each 44 blocks. These motion vectors are determined further that if the neighboring blocks could be merge into other predefined block types or not. Because the motion search is performed only at the smallest block size 44, the computational load of other block size motion estimation can be saved. While in RD optimized mode decision between inter and intra mode, the threshold decision method is adopt to save more computation load for inter - 168, 816, 88 and intra mode. The simulation results show that the performance of this simple algorithm is close to H.264 video encoder JM7.3 with all block modes -1616, 168, 816, 88, 84, 48, and 44 are enabled while the computational complexity is significantly reduced.

    CONTENTS LIST OF TABLES i LIST OF FIGURES i i CHAPTER 1 Introduction 1 CHAPTER 2 Overview of H.264 JM7.3 Encoder 4 2.1 Inter Prediction 8 2.1.1 Tree Structured Motion Compensation 8 2.1.2 Motion Vector Prediction 10 2.1.3 Fractional Sample Accuracy Motion Vectors 12 2.1.3.1 Generating Interpolated Samples 13 2.2 Intra Prediction 16 2.2.1 4×4 Luminance Prediction Modes 17 2.2.2 16×16 Luminance Prediction Modes 19 2.2.3 8×8 Chrominance Prediction Modes 20 2.3 Rate Distortion Optimization Routine 20 2.3.1 The Rate Distortion Optimized Routine for Motion Estimation 21 2.3.2 The Rate Distortion Optimized Routine for Mode Decision 22 CHAPTER 3 The Proposed Algorithm 23 3.1 The Merging Process 24 3.1.1 Candidate Motion Vectors for Merge Process 27 3.2 Decide the Optimal Block Mode for Macroblock 31 3.2.1 Threshold Decision Method 34 CHAPTER 4 Experimental Results 38 4.1 Assessment 38 4.2 Experiment Results 38 CHAPTER 5 Conclusion and Future Works 45 REFERENCE 46 BIOGRAPHY 48

    REFERENCE

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