賴飛羆臺灣大學：資訊工程學研究所蔡豐旭Tsai, Feng-HsuFeng-HsuTsai2007-11-262018-07-052007-11-262018-07-052005http://ntur.lib.ntu.edu.tw//handle/246246/53754在進行視訊壓縮時，減少因為移動估計演算法所產生的龐大計算量是必要的。在這篇論文中我們提出利用視訊畫面中所具有的運動特性來調適搜尋時用到的視窗。在提出的架構中，我們藉由分別建立水平和垂直方向的運動模型來將搜尋視窗縮減成矩形來取代方型搜尋視窗。在此種架構下，我們得到更簡化而適合的搜尋視窗。甚者，適合在與此演算法搭配的預測性行搜尋的管線式硬體架構也在這篇論文中被提出,並應用在實驗中。實驗數據顯示出，在跟原本的預測性行搜尋比較下，將運動特性應用上去可以節省約40%到50%的計算量，並且以PSNR衡量的影像品質來說，也能夠有相當接近的表現。To reduce the huge computation cost of motion estimation is indispensable in practical encoding system. Utilizing the motion activity of video sequence to adapt the search window is proposed in this thesis. The proposed scheme provides a precise estimation of two motion activities in horizontal and vertical directions to refine the search window in a shape of rectangular, instead of a square search window. Hence, we can find a simpler yet more appropriate search window. Furthermore, an efficient pipeline architecture designed for PLS algorithm, which is hybridized with our proposed algorithm, is discussed in this thesis and is implemented in our experiments. Experimental results show that 40% to 50% performance improvement in speed is gained while yielding nearly the same quality measured in PSNR compared to the PLS.Chapter 1 Introduction 1 1.1 Background 1 1.1.1 What is Motion Estimation and Motion Compensation 1 1.1.2 Temporal Redundancy 2 1.1.3 Motion Vector 3 1.1.4 Block Matching 4 1.1.5 Matching Criteria 4 1.2 The Scheme of Motion Estimation and Compensation 5 1.3 Motivation 6 1.3.1 Consideration of Hardware 7 1.3.2 Information of Motion Activity 7 1.3.3 Discussions 8 Chapter 2 Related Work 9 2.1 Predictive Line Search (PLS) 9 2.1.1 Motion Vector Prediction 9 2.1.2 Detailed Algorithm 12 2.2 Adaptive Search Window (ASW) 13 2.2.1 Introduction 14 2.2.2 Global Motion Activity (GMA) 14 2.2.3 Local Motion Activity (LMA) 17 2.2.4 Discussions 18 Chapter 3 Proposed Method 20 3.1 Refined Adaptive Search Window (RASW) 20 3.1.1 Observation 20 3.1.2 Refined Global Motion Activity (RGMA) 21 3.1.3 Refined Local Motion Activity (RLMA) 25 3.1.4 Implementation Detail 28 3.1.4.1. Estimation of RGMA 28 3.1.4.2. Computation of Local Motion Activity 29 3.1.4.3. Adaptation of Search Range 29 3.2 Implementation Issues for PLS HW Design 30 3.2.1 Overview of the Architecture 31 3.2.2 Data flow 33 3.2.3 Special Features 37 3.2.4 Efficiency Analysis 38 3.3 Enhanced Predictive Line Search Using Refined Adaptive Search Window 39 Chapter 4 Experiment and Results 40 4.1 Implemented Method 40 4.2 Experimental Environment 41 4.3 Experimental Results 42 Chapter 5 Conclusion 57 Reference 582472436 bytesapplication/pdfen-US移動估計可適性搜尋視窗管線式硬體架構motion estimationadaptive search windowpipeline architecturethesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/53754/1/ntu-94-R92922115-1.pdf