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研究生:蔡雅婷
研究生(外文):Ya-Ting Tsai
論文名稱:利用方向小波轉換分析進行視訊編碼之位元分配
論文名稱(外文):Curvelet Domain Analysis for Video Coding Bit Allocation
指導教授:蔡淳仁
指導教授(外文):Chun-Jen Tsai
學位類別:碩士
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:2007
畢業學年度:95
語文別:英文
論文頁數:67
中文關鍵詞:方向小波轉換位元分配結構性影像
外文關鍵詞:curvelettransformbit allocationstructured image
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本論文主旨在於探討人類視覺特性於視訊編碼中之位元配置方式,並根據論文中提出的方法利用方向小波轉換分析視訊影片中的原始圖像及殘值圖像的結構性特徵,且依此分析結果設計一個位元分配的策略,使得在視覺上較為重要的區域可得到較多的位元,以期達到較佳的視覺品質。本論文中使用方向小波轉換分析的主因為它可在不同方向上做子頻帶分解,所以相較於離散餘弦轉換或其他基於可分離轉換的小波轉換,方向小波轉換能顯現出更多的結構性資訊。論文中提出的位元分配的策略嘗試在非結構性區域節省位元及在結構性區域增加視覺品質。在MPEG-4簡易版類別編碼器上的實驗結果顯示,提案方法在所有測試案例中皆有較好的表現,能在人眼視覺較為重視的區域得到較佳的影像品質。
This paper proposes a video bit allocation scheme based on Curvelet domain analysis. The proposed algorithm analyzes the structural characteristics of the intensity and motion-compensated residual images of a video sequence in curvelet domain to determine a bit-allocation policy so that visually important regions will be allocated with more bits. Curvelet transform is adopted in this thesis for such analysis because it performs sub-band decomposition in various directions so that more structure information is revealed in curvelet domain than in DCT or other wavelet domains based on separable transforms. The proposed bit-allocation policy tries to save bits in unstructured regions and increase quality in structured regions. Experiments using standard test sequences coded with an MPEG-4 simple profile video encoder show that the proposed bit allocation method has better performance (achieves higher PSNR’s) in the regions most human observers care about in all test cases.
1. Introduction 1
2. Previous Work 4
2.1. Transform Analysis 4
2.2. Properties of Human Visual System 7
3. Study and Analysis of Curvelet Transform 10
3.1. Why Curvelet Transform 10
3.2. Fundamentals of Curvelet Transform 11
3.3. Mathematical Formulation of Curvelet Transform 13
3.4. Implementation of Digital Curvelet Transform 17
3.4.1. Take Fourier transform into frequency domain 18
3.4.2. Band-pass filtering 19
3.4.3. Polar interpolation 24
3.4.4. Inverse Fourier transform 26
3.5. Interpretation of the Curvelet Transform Coefficients 26
3.5.1. Curvelet Coefficients in the Coarsest and the Finest level 27
3.5.2. Curvelet Coefficients in the Middle Levels of Resolutions 27
4. Proposed Bit Allocation Framework 30
4.1. Analysis on curvelet transform coefficients 30
4.1.1. The composition of the curvelet transform 31
4.1.2. Image type and the presentation of the related coefficient 33
4.2. The Otsu Algorithm 35
4.3. Statistical method to analyze the coefficients 38
4.4. Proposed Bit Allocation Scheme 41
4.5. Determination of the Weighting Threshold 43
5. Experiment and Analysis 46
5.1. Result of the proposed bit allocation scheme 46
5.2. Result of Proposed Bit Allocation Scheme with Weighting Threshold 52
5.2.1. Number of structured and unstructured blocks of the two method 53
5.2.2. The result of proposed scheme with linear formula 54
6. Conclusion and Future Work 62
7. Reference 65
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