臺灣博碩士論文加值系統

現今對於多媒體影音方面的要求越來越大，且畫面品質要求也越來越高，因此多媒體影音格式的制定一直是很熱門的話題和研究。逐漸崛起的影音格式H.264 利用很多不同以往的編碼和預測方式，可容易達到高品質且壓縮位元率的目的。但也因此需要付出很重的運算複雜度來達成，因此減少複雜度且可維持一定品質的方法，即為我們此論文的探討內容。
我們首先提出不同以往架構的多幅參考畫面候選模式的演算法，可使其計算量可以節省30%左右，然後又運用比較精準的向量預測方式，加上可變搜尋視窗的方法，來達到縮小搜尋視窗的目的，又可再多節省不少時間的運算，平均差不多50%左右的時間節省，最後結合所有的方法，可達到節省70%左右。
實驗結果顯示我們不僅可達到一定時間的節省，但是畫面品質也可在可接受範圍的損失，所以運用到即時(Real Time)系統上運用，應該是可行的方法。

The emerging popular video coding standard, called H.264/AVC, supports many advanced techniques to achieve better performance compared to the previous one. As the performance got better and better, the computation load
must be paid as return. In order to make it apply in the real-time system as possible, so many researched results had been proposed to reduce the complexity. In this thesis, we also proposed some algorithms to achieve the
same goal. First, we proposed how to distinguish candidate modes from the others and terminated other frames of the candidate modes. And then we combined the predictive motion vector with the adaptive search window to
shrink the search process. Finally, we integrated these three proposed methods into the whole novel multi-frame motion estimation algorithm.
The simulation result showed that we could save for 70% in coding time with insignificant performance loss.

第一章 緒論
1.1 國際視訊壓縮標準簡介 1
1.2 動機與目的 2
1.3 論文架構 4
第二章 多幅參考畫面之候選模式選擇
2.1 移動估計簡介 5
2.1.1 可變區塊大小的移動估計 10
2.1.2 非整數點的移動估計 11
2.1.3 多幅參考畫面的移動估計 12
2.2 回顧多幅參考畫面 13
2.3 多幅參考畫面之統計分析 17
2.4 多幅參考畫面之候選模式選擇演算法 19
2.5 效能分析
2.5.1 固定量化參數下的編碼效能比較 26
2.5.2 不同量化參數下之編碼效能比較 26
2.6 多幅參考畫面之候選模式選擇演算法之結論 35
第三章 各參考畫面初始向量預測演算法
3.1 向量預測簡介 36
3.2 向量預測之回顧與統計分析 39
3.3 混合型向量預測演算法 42
3.4 效能分析
3.4.1 固定量化參數下的編碼效能比較 48
3.4.2 不同量化參數下之編碼效能比較 49
3.5 各參考畫面初始向量預測演算法之結論 51
第四章 新的多幅參考畫面移動估計演算法
4.1 可變搜尋視窗簡介 52
4.2 可變搜尋視窗演算法 54
4.3 效能分析
4.3.1 固定量化參數下的編碼效能比較 55
4.3.2 不同量化參數下之編碼效能比較 56
4.4 新的多幅參考畫面移動估計演算法 58
4.5 效能分析
4.5.1 環境參數設定 59
4.5.2 固定量化參數下的編碼效能比較 60
4.5.3 不同量化參數下之編碼效能比較 61
4.6 新的多幅參考畫面移動估計演算法之結論 65
第五章 結論和未來展望 66
參考文獻 67
中英對照表 70

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