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研究生:艾斯拉夫
研究生(外文):ashraf
論文名稱:移動向量精煉新方法運用於視訊處理和視訊轉換編碼的研究
論文名稱(外文):Study on Motion Vector Refinement in Video Processing and Transcoding Applications
指導教授:李素瑛李素瑛引用關係
指導教授(外文):Suh-Yin Lee
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊科學與工程研究所
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:2006
畢業學年度:94
語文別:英文
論文頁數:124
中文關鍵詞:影像處理
外文關鍵詞:Video Processing
相關次數:
  • 被引用被引用:0
  • 點閱點閱:140
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在這篇論文中,我們提出了修正視訊串流中移動向量特徵的新方法,並且實作幾個以移動向量為基礎的應用。我們將討論與驗證在MPEG視訊串流中的物件偵測與擷取,以物件為基礎的視訊串流,以及在有限資源環境中的視訊轉換編碼。我們對直接由壓縮域中的視訊串流擷取出的位移向量做處理,以降低移動向量的雜訊並獲得更多的物件資訊。透過我們所提出的系統,包含空間資訊過濾元件、時間資訊過濾元件以及紋路過濾元件,可以使這些物件資訊變得更精確。此外我們針對特定的應用提出了適應性移動向量修正的方法。因此,我們提出的視訊處理與通訊演算法可以準確地取得使用者想要的結果,在執行時間上也更有效率。我們也針對系統效能與其他常用的相關研究及技術作比較。
我們針對MPEG7測試資料與額外的視訊測試序列(超過200小時)來進行實驗,並用標準的指標評估系統效能,實驗結果顯示我們提出的系統效能明顯的優於其他技術。我們也介紹了新的效能評估指標來正確地追蹤我們的系統效率。
除此之外,本篇論文中也描述了我們發展的使用者介面,讓使用者可以維護與監控演算法的執行過程。
In this thesis we propose novel approaches for refining motion vector features in video streams. Several motion vector based applications have been proposed and implemented. Object detection and extraction in MPEG video streams, object based video steaming, and video transcoding with resource limited environment are all discussed, designated and verified. Rather than processing the extracted motion vector fields directly extracted from video streams in the compressed domain, we perform several operations over the extracted motion vectors, in order to reduce the noise within the motion vector content and to obtain more robust object information. The information is refined through our proposed system which is composed of a spatial filter component, temporal filter component and texture filter component. In addition, an adaptive motion vector refinement has been proposed for specific applications. As a result, our proposed video processing schemes are more capable of accurately gaining desired results with more efficient performance in terms of runtime. We compare the performance of our proposed system with other popular and commonly related work and techniques.
Based on the experimental results performed over the MPEG7 testing dataset and additional video testing sequences –more than 200 hours of videos-, the performance measured by standard performance metrics using our proposed system is superior to the alternative techniques. Moreover, we introduce new performance metrics to trace the efficiency of the proposed systems accurately.
A user system interfaces is also presented, where users can maintain and monitor the process of the proposed algorithms.
Table of Content
Abstract in Chinese I
Abstract in English III
Acknowledgment V
Table of Contents VI
List of Tables X
List of Figures XI
Chapter 1 Introduction 1
1.1 Processing in compressed domain 1
1.2 Motion vector based video processing and communication applications 3
1.3 Organization of the sis 7
Chapter 2 Background and Related Work 8
2.1 Background information for MPEG and motion vectors 8
2.1.1 Motion Compensation 11
2.1.2 DCT Transform 13
2.2 Related Work 14
Chapter 3 Motion Vector Analysis 16
3.1 Motion Vector Noise Analysis 16
3.2 Motion vector limitations 17
3.3 Motion Vector Noise Model 19
Chapter 4 Motion Vectors Refinement Approaches 21
4.1 Gaussian Based Motion Vector Refinement 21
4.1.1 Smoothing of Motion Vector Field 22
4.2 Cascade Filter Based Motion Refinement 26
4.3 Temporal Based Motion Vector Refinement 26
4.4 Texture Based Motion Vector Refinement 27
4.4.1 Texture energy computation 28
4.4.2 Reconstruction of DC Images 34
4.5 Overview of the Motion Vector Refinement System 36
4.6 Adaptive Motion Vector Refinement 36
4.6.1 Noise Power Estimator 39
Chapter 5 Object detection and extraction from video streams 41
5.1 Introduction 41
5.2 Related work and Background 43
5.3 Object Detection and Extraction 44
5.3.1 Object Detection Algorithm 44
5.3.2 Edge Detection using AC coefficients 46
5.4 Object detection overview 47
Chapter 6 Fast and Robust Object Extraction Framework for Object Based Streaming
System 50
6.1 Introduction 50
6.2 Related Work 55
6.3 Object based Video Streaming 56
6.4 Object Extraction System 61
6.5 RTP Sender and Receiver 62
6.6 Experimental Results and Discussion 64
6.7 Future Work 67
6.8 Conclusions 68
Chapter 7. A Novel Approach for Improving the Quality of Service for Wireless Video Transcoding 70
7.1 Introduction 70
7.2 Related Work 73
7.3 Mobile and Wireless Video Transcoding System Architecture 77
7.4 Motion Estimation in Transcoding 81
7.5 Motion Vector Refinement 82
7.6 Result and Discussion 84
7.7 Conclusion and Future work 89
Chapter 8 Experimental Results and Discussion 91
8.1 Objective Evaluation 91
8.1.1 Testing dataset configurations 92
8.1.2 Discussion 99
8.2 Subjective Evaluation 100
8.3 Run Time Evaluation 106
Chapter 9 Conclusion and Future Work 108
9.1 Conclusion 108
9.2 Future Work 109
References 111
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