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研究生：	李信德 Li, Hsin-Te
論文名稱：	使用邊緣圖形辨識之動態適應性解交錯掃瞄器 Motion Adaptive Deinterlacing Using Edge Pattern Recognition
指導教授：	李國君 Lee, Gwo Giun
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：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	97
中文關鍵詞：	解交錯掃瞄、邊緣圖形辨識
外文關鍵詞：	edge pattern recognition, deinterlacing
相關次數：	點閱：90 下載：4
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　　本論文主旨為提出一個動態適應性之解交錯演算法與其硬體之實現。動態適應性之解交錯方法可以有效地結合空間與時間上之圖像處理方法的優點而得到良好的結果。本論文所提出的解交錯演算法是先淬取出在各禎圖(Field)中的區塊特性，再利用邊緣圖形辨識方法(Edge Pattern Recognition)來對所缺少的掃瞄線進行補點的動作。邊緣圖形辨識的方法對於材質的補點結果相當有幫助。在動態偵測方法上，本論文提出了混合式的動態偵測方法(Hybrid Motion Detection)，可以有效地減少動態誤判的問題。接著再利用物體邊緣形態處理來對混合式動態偵測的結果進行後處理的步驟，以減少雜訊並增進動態偵測結果之可靠性。有了事先淬取出來的各區塊特性，每個點的動態狀況就可以更準確地判斷出來。

　　本論文所提出的硬體架構設計總共有五個管線階層。第一階層是處理混合式動態偵測方法與殘值直方圖(Residue Histogram)的計算。在第二階層會進行侵蝕(Erosion)與三禎圖動態偵測法(Three-field Motion Detection)的結果精煉的步驟。第三階層則包含了擴散(Dilation)的後處理步驟。第四階層則進行邊緣圖形辨識與動態結果判定的步驟。第五階層則是處理解交錯掃瞄器的輸出。本論文所提出的硬體架構是利用UMC .18 um之製程來進行合成，操作的頻率為81百萬赫茲。硬體架構是在由Mentor Graphics Seamless軟體所提供的ARM平台，並在軟硬體共同模擬的環境下進行驗證。

　This thesis presents a motion adaptive deinterlacing algorithm and its hardware implementation. Motion adaptive deinterlacing can be used to effectively combine the benefits of both interframe and intraframe processing with good results. The proposed deinterlacing algorithm extracts the features of blocks within the fields and uses edge pattern recognition method to interpolate the missing scan lines. The edge pattern recognition method is beneficial for texture interpolation. The proposed hybrid motion detection method can reduce the motion missing problem efficiently. A morphological post-processing step is applied to the results of hybrid motion detection to reduce the noises and increase the robustness of motion detection. With the features of blocks, the motion status of each pixel can be decided more precisely.

　There are five pipeline stages in the proposed hardware architecture. The first stage deals with hybrid motion detection and residue histogram calculating. Erosion and three-field motion detection refinement are performed in the second stage. The third stage contains dilation processing. The edge pattern recognition and motion status decision are performed in the fourth stage. The fifth stage deals with the deinterlacer output. The proposed architecture is synthesized with UMC .18 m technology. The design is operating at the speed of 81MHz. The proposed design is also verified under the software/ hardware co-simulation environment on an ARM-based platform using Seamless provided by Mentor Graphics.

Abstract ......................................................................i
List of Figures .............................................................vii
List of Tables ................................................................x
Chapter1　Introduction ........................................................1
　1.1 Background Introduction .................................................1
　1.2 Organization of this thesis .............................................4
Chapter 2 Deinterlacing Algorithms ............................................5
　2.1 Problem Statement for Deinterlacing .....................................5
　2.2 Non-Motion Compensated Methods ..........................................8
　　2.2.1 Spatial Methods .....................................................8
　　2.2.2 Temporal Methods ...................................................13
　　2.2.3 Spatial-temporal Methods ...........................................14
　　2.2.4 Motion Adaptive Methods ............................................16
　2.3 Motion Compensated Methods .............................................17
Chapter 3 Motion Adaptive Deinterlacing ......................................18
　3.1 Motion Detection in Interlaced Sequences ...............................18
　　3.1.1 Two-Field Motion Detection .........................................20
　　3.1.2 Three-Field Motion Detection	......................................22
　　3.1.3 Four-Field Motion Detection ........................................24
　　3.1.4 Five-Field Motion Detection ........................................26
　　3.1.5 Other Methods ......................................................29
　3.2 Motion Detection with Adaptive Motion Parameters .......................30
Chapter 4 Proposed Deinterlacing Algorithm ...................................32
　4.1 Motion Detection .......................................................33
　　4.1.1 Hybrid Motion Detection ............................................33
　　4.1.2 Refinement for Motion Detection ....................................37
　　4.1.3 Retrieve Local Block Features ......................................39
　　4.1.4 Final Motion Status Decision	......................................42
　4.2 Interpolation ..........................................................43
　　4.2.1 Edge Pattern Recognition ...........................................43
　　4.2.2 Edge Pattern Recognition for Deinterlacing .........................47
Chapter 5 Architecture Design and Implementation .............................48
　5.1 Specification Definition ...............................................48
　5.2 Architecture Design ....................................................49
　　5.2.1 Data Flow ..........................................................49
　　5.2.2 Pipeline Design ....................................................53
　　5.2.3 Memory Configurations ..............................................56
Chapter 6 Verification & Experimental Results ................................58
　6.1 Design Flow ............................................................58
　6.2 Verification ...........................................................60
　　6.2.1 Basic Unit Verification ............................................60
　　6.2.2 Modulized Pipeline Stage Verification ..............................61
　　6.2.3 System Verification ................................................62
　　6.2.4 Hardware/Software Emulation ........................................63
　6.3 Experimental Results ...................................................69
　　6.3.1 Experimental Results for Texture Analysis without Motion Detection  69
　　6.3.2 Experimental Results for Motion Detection Analysis .................72
　　6.3.3 PSNR Comparison & Subjective View Comparison .......................74
　　6.3.4 Implementation Results and Summary .................................76
Chapter 7 Conclusions and Future Work ........................................79
　7.1 Conclusions ............................................................79
　7.2 Future Work ............................................................80
Bibliography .................................................................82
Appendix .....................................................................86
                                    

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校外：2006-01-26公開

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