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研究生:許正杰
研究生(外文):Cheng-Chieh Hsu
論文名稱:利用方向性內插法以及運動補償內插法之混合式去交錯系統
論文名稱(外文):Hybrid De-interlacing System using Directional Interpolation and Motion Compensation
指導教授:陳永昌陳永昌引用關係
指導教授(外文):Yung-Chang Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:77
中文關鍵詞:去交錯處理運動補償方向性內插
外文關鍵詞:De-interlacingMotion compensationDirectional interpolation
相關次數:
  • 被引用被引用:1
  • 點閱點閱:174
  • 評分評分:
  • 下載下載:32
  • 收藏至我的研究室書目清單書目收藏:0
摘要

交錯式掃描的技術被使用在傳統的電視系統上以節省傳送資料的頻寬,但是交錯式掃描的信號會產生視覺效果上的不良效應,如邊緣閃爍,線條閃爍,線條效應。去交錯處理是一種能將交錯掃描信號轉換成步進式掃描信號的技術,這個技術已經被廣泛地使用來降低交錯式掃描信號的不良視覺效應,而近年來出產的高畫質電視能支援步進式掃描信號的輸出,因此為了得到較佳的視覺效果,我們需要研發較佳的去交錯處理技術。在這篇論文裡,我們提出了一個以方塊為基礎的去交錯處理演算法。
提出的以方塊為基礎的去交錯演算法結合了時間運動補償法和空間去交錯補償法,並且提供了能對抗快速運動物體的強健性。對於每個被處理的影像方塊,我們先偵測這個方塊的移動特性,再依照不同的特性做不同方式的去交錯處理,對於快速移動的區塊,我們用大量的空間去交錯處理,對於慢速移動或靜止的區塊,我們用大量的運動補償和時間去交錯處理。對於影像中複雜度較高的部分,我們也使用運動補償和時間去交錯處理。此外,我們的演算法有做攝影機移動的偵測,在攝影機移動的情況之下我們使用運動補償去交錯處理以得到較好的輸出效果。
根據實驗結果,和傳統的多去交錯處理演算法相比,我們提出的去交錯處理演算法對於大部分影片可以得到比較好的去交錯效果。對於某些測試資料,我們的方法甚至能比傳統的方法提高10dB以上的PSNR。
Abstract

Interlaced scanning technique is used for traditional television system to save the bandwidth of the transmitted data. But, uncomfortable visual artifacts such as edge flicker, line crawling, and interline flicker occur due to the inherent nature of the interlaced scanning process. De-interlacing is a technique which can convert the interlaced pictures to progressive pictures. This technique has been widely used to reduce the visual artifact caused by interlaced scanning process. Moreover, recent HDTV systems support the progressive scan to improve the visual quality. Thus, we have to find a good de-interlaced algorithm to get better visual quality. In this thesis, a blocked based de-interlaced algorithm is proposed.
The proposed block-based de-interlacing method combines motion compensated scheme with spatial de-interlaced scheme and provides better robustness for fast moving object. For each to-be-processed block, we first detect the motion property of this block, and then perform suitable de-interlaced process for each empty pixel in this block. In the case of fast moving block, we use largely spatial de-interlacing. In the case of slow moving or static block, we use largely motion compensated de-interlacing and temporal de-interlacing. For complex regions in an image, motion compensated de-interlacing is performed. Moreover, the proposed algorithm provides camera motion frame detection. For all empty pixels in a camera motion frame, we use motion compensated de-interlacing to get better performance.
From the simulation result, we see that the proposed de-interlacing algorithm can get better performance than that of traditional algorithms. Our method can even outperform traditional methods about 10dB for some test sequences.
Table of Contents
Abstract i
Table of Contents ii
List of Figures iv
List of Tables vi

Chapter 1: Introduction 1
1.1 Overview of interlacing 1
1.2 Motivation 2
1.3 Thesis organization 2

Chapter 2: Overview of De-interlacing Algorithm 4
2.1 The problems and objectives 4
2.2 Conventional de-interlacing systems 8
2.2.1 Non-motion-compensated de-interlacing 8
2.2.2 Motion-compensated de-interlacing 13
2.2.3 Hybrid method 17
2.3 The main challenge 20

Chapter 3: Hybrid Method using Directional Interpolation and Motion Compensation 20
3.1 Algorithm of the proposed method 20
3.1.1 Block-based robust motion detection 20
3.1.2 Two-step motion search with fast motion detection 22
3.1.3 Motion vector smoothness 25
3.1.4 Complex region detection 26
3.1.5 Camera motion detection 27
3.1.6 Directional interpolation using ELA 28
3.1.7 Interpolation method 29
3.1.8 Summary of the proposed algorithm 33
3.2 Architecture design of the proposed algorithm 36
3.2.1 Field buffers and block buffers 37
3.2.2 Field-based processor 40
3.2.3 Block-based de-interlacing module 41
3.2.4 Summary of the proposed architecture 55

Chapter 4: Simulation Result 56
4.1 Simulation environments 56
4.1.1 Measurement system 56
4.1.2 De-interlacing parameters 57
4.2 Simulation result 57
4.2.1 Foreman sequence 58
4.2.2 Container sequence 60
4.2.3 Mother and daughter sequence 61
4.2.4 Stefan sequence 63
4.2.5 Flower garden sequence 65
4.2.6 Coastguard sequence 67
4.2.7 Pendulum sequence 69
4.3 Summary 73

Chapter 5: Conclusions and Future Works 74
5.1 Conclusions 74
5.2 Future works 75

References 76


List of Figures
Fig. 1-1 Interlaced video 1
Fig. 1-2 The process of de-interlacing 3
Fig. 2.1-1 The sampling lattices of progressive scanning and interlaced scanning 4
Fig. 2.1-2 The carrier lattices of progressive scanning and interlaced scanning 5
Fig. 2.1-3 The replications of the continuous spectrum after progressive sampling and interlaced sampling 6
Fig. 2.1-4 The spectrums of video signals after sampling 6
Fig. 2.2-1 The required frequency domain passbands of VT-filtering 9
Fig. 2.2-2 VT filtering de-interlacing technique 9
Fig. 2.2-3 Motion adaptive de-interlacing algorithm 10
Fig. 2.2-4 An example for block-based motion detection 10
Fig. 2.2-5 A local window for the ELA interpolation 11
Fig. 2.2-6 De-interlacing using motion compensated field insertion 13
Fig. 2.2-7 De-interlacing using motion compensated average 14
Fig. 2.2-7 Switching of motion vector 15
Fig. 2.2-9 De-interlacing using hybrid method proposed in [11] 17
Fig. 2.2-9 De-interlacing using MCFI proposed in [3] 18
Fig. 3.1-1 (a) The conventional block-based motion detection method 21
Fig. 3.1-1 (b) Misjudgement results from fast motion object 21
Fig. 3.1-2 The proposed robust motion detection 21
Fig. 3.1-3 An example of video sequence with fast moving object 22
Fig. 3.1-4 Two-step motion with fast motion detection 23
Fig. 3.1-5 Bi-directional refined search 24
Fig. 3.1-6 Neighboring blocks used for MV smoothing 25
Fig. 3.1-7 Complex region detection 26
Fig. 3.1-8 Camera motion detection 28
Fig. 3.1-9 The seven-point ELA algorithm 29
Fig. 3.1-10 Fast motion information detection 30
Fig. 3.1-11 Input pixels of median filter 30
Fig. 3.1-12 Deteermination of “BadMotion” and “Edge” 32
Fig. 3.1-13 Flow chart of choosing final interpolation method 32
Fig. 3.1-14 Flow chart of proposed algorithm (a) 34
Fig. 3.1-15 Flow chart of proposed algorithm (b) 35
Fig. 3.2-1 Block diagram of the proposed architecture 36
Fig. 3.2-2 The architecture of a field-buffer 37
Fig. 3.2-3 Connection between frame buffers and field buffers 38
Fig. 3.2-4 Block buffer 39
Fig. 3.2-5 Flow chart of “MBcounter” controller 40
Fig. 3.2-6 Block diagram of “block-based de-interlacing” module 41
Fig. 3.2-7(a)(b) IO ports and block diagrams of “Motion Detection module” 41
Fig. 3.2-7(c) Architecture of “Motion Detection processor” 43
Fig. 3.2-8 IO ports of Initial motion search module and Refined search module 43
Fig. 3.2-9 Block diagram of “Initial motion search” module 44
Fig. 3.2-10 Architecture of “MVI processor” 45
Fig. 3.2-11 Block diagram of “Refined motion search” module 46
Fig. 3.2-12 Architecture of “MVR processor” 47
Fig. 3.2-13 IO ports of “MV smoothing” module 48
Fig. 3.2-14 IO ports of “MV smoothing” module 49
Fig. 3.2-15 Architecture of “MVS processor” 49
Fig. 3.2-16 IO ports of camera motion detection 50
Fig. 3.2-17 Block diagram of camera motion detection module 51
Fig. 3.2-18 Architecture of “CPX Processor” module 51
Fig. 3.2-19 Functions of “J” and “counter” 52
Fig. 3.2-20 Block diagram of the “ELA & Median interpolation” module 52
Fig. 3.2-21 The block diagram of “Processor_N” module 53
Fig. 3.2-22 The block diagram of “ELA” module 54
Fig. 3.2-23 The block diagram of “OutPixel” module 54
Fig. 4-1 Performance measurement by re-converting 56
Fig. 4.2-1 The PSNR performance of the four methods on the “Forman” sequence 58
Fig. 4.2-2 154th reconstructed image of test sequence “Foreman” 59
Fig. 4.2-3 The PSNR performance of the four methods on the “container” sequence 60
Fig. 4.2-4 264th reconstructed frame of test sequence “Container” 61
Fig. 4.2-5 The PSNR performance of the four methods on the “Mother and daughter” sequence 62
Fig. 4.2-6 58th reconstructed image of test sequence “Mother and daughter” 61
Fig. 4.2-7 The PSNR performance of the four methods on the “Stefan” sequence 63
Fig. 4.2-8 176th reconstructed image of test sequence “Stefan” 64
Fig. 4.2-9 The PSNR performance of the four methods on the “Flower garden” sequence 65
Fig. 4.2-10 13th reconstructed image of test sequence “Flower Garden” 66
Fig. 4.2-11 The PSNR performance of the four methods on the “Coastguard” sequence 67
Fig. 4.2-12 220th reconstructed image of test sequence “Coastguard” 68
Fig. 4.2-13: The 4th (a) and 5th (b) interlaced images of the “Pendulum” sequence 69
Fig. 4.2-14: Reconstructed images of the “Pendulum” sequence using different methods 70
Fig. 4.2-15: Reconstructed images of the “Toilet paper” sequence. 71
Fig. 4.2-16: Reconstructed images of the “Flag” sequence. 72





List of Tables
Table. I The parameters of the proposed algorithm 57
Table. II PSNR comparison 73
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