利用影像運動歷史之快速移動估測演算法｜國立臺灣科技大學博碩士論文系統

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研究生：	徐誌鴻 Chih-hung Hsu
論文名稱：	利用影像運動歷史之快速移動估測演算法 Using Motion History Image for ProposedFast Motion Estimation Algorithm
指導教授：	王乃堅 Nai-Jian Wang
口試委員:	劉昌煥 Chang-Huan Liu 呂學坤 Shyue-Kung Lu 鍾順平 Shun-Ping Chung 蔡超人 Chau-Ren Tsai
學位類別：	碩士 Master
系所名稱：	電資學院 - 電機工程系 Department of Electrical Engineering
論文出版年：	2010
畢業學年度：	98
語文別：	中文
論文頁數：	59
中文關鍵詞：	移動估測、影像運動歷史、移動向量
外文關鍵詞：	Motion Estimation, Motion History Image, Motion Vector
相關次數：	點閱：139 下載：6
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在視訊編碼的架構中，移動估測(Motion Estimation) 係利用連續影像序列間的相關性去除畫面間的冗餘，以達到壓縮的效果；移動估測演算法（Fast Motion Estimation Algorithm）的好與壞往往直接影響到編碼後影像的品質及其編碼速度，許多學者紛紛提出快速的移動估測演算法，來降低運算量並維持品質。
AHMEA（Adaptive Hierarchical Motion Estimation Algorithm）演算法於2005年被提出，其原理在於利用影像運動歷史（Motion History Image, MHI）及區塊層的運動強度(Block Level Motion Intensity)作為判斷候選向量的準則，更迅速地找到移動向量(Motion Vector, MV)。
然而，AHMEA演算法在給予候選向量的分類不佳，使得區塊間移動向量差異大的反而選擇性少。本論文以AHMEA演算法為基礎，改良原論文中候選向量的分類及預測方法，並加入2009年發表的FDGDS（Fast Directional Gradient Descent Search)演算法做為搜尋移動向量的方法。實驗結果顯示，我們所提出的演算法相較於原作法和其它數種快速搜尋演算法，除了能以較快的速度找到移動向量，並且維持壓縮後影像的品質。

During the procedure of video coding, we use Motion Estimation (ME), which utilizes the correlation between video frames, to remove the redundancy between adjacent video sequences. As a result, it directly influences the quality of coded video and coding performance. To reduce computation and maintain a good video quality, many fast search algorithms have been proposed.
Adaptive Hierarchical Motion Estimation Algorithm (AHMEA), proposed in 2005, uses Motion History Image (MHI) and Block Level Motion Intensity to decide the set of candidate vectors. By choosing candidate vectors efficiently, the performance of Motion Vector (MV) prediction will be improved
However, the classification of candidate vectors in AHMEA raises new problems. The options of candidate vector reduce as the diversity between motion vectors of neighborhood blocks rises. That is, the more distinct the set of neighboring block motion vectors are, the less candidates we can choose from, which is obviously contrary to the general rules. As a result, in this thesis we propose an algorithm based on the motion history image from AHMEA, improving the classification and prediction of candidate vectors. In addition, we use the Fast Directional Gradient Descent Search (FDGDS) algorithm to search the motion vector. According to the result of experiments, our algorithm can not only reduce computational complexity but also maintain the quality of encoded video sequences.

摘要	......................................................i
ABSTRACT	.....................................................ii
誌謝	....................................................iii
目錄	.....................................................iv
圖表目錄	.....................................................vi
第一章	緒論 .................................................1
1.1 研究動機...................................................1
1.2 研究背景及方法 ............................................2
1.3 論文組織 ..................................................3
第二章	視訊編碼架構與快速移動估測演算法......................5
2.1 視訊編碼標準簡介 ..........................................5
2.2 視訊編碼架構簡介 ..........................................7
2.3 移動估測 ..................................................9
2.4 快速搜尋演算法 ...........................................11
    2.4.1  三步搜尋演算法 ....................................12
    2.4.2  新三步搜尋演算法 ..................................13
    2.4.3  四步搜尋演算法 ....................................14
    2.4.4  菱形搜尋演算法 ....................................17
    2.4.5  MVFAST搜尋演算法 ..................................20
第三章	AHMEA搜尋演算法以及所提出改良之演算法 ...............24
3.1 AHMEA搜尋演算法 ..........................................24
    3.1.1  影像運動歷史 ......................................24
    3.1.2  區塊層的運動強度 ..................................26
    3.1.3  移動向量的預測 ....................................27
    3.1.4  搜尋方法 ..........................................29
3.2 利用影像運動歷史之快速移動估測演算法 .....................31
    3.2.1  區塊間的運動關係程度及移動向量的預測 ..............31
    3.2.2  搜尋方法 ..........................................35
第四章	實驗結果與分析 ......................................39
4.1 實驗介紹 .................................................39
4.2 實驗分析 .................................................41
第五章	結論與未來展望 ......................................47
5.1 結論 .....................................................47
5.2 未來展望 .................................................47
參考文獻 .....................................................48
作者簡介 .....................................................52

                                

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