在這篇論文中，我們提出一個快速並且能夠找到最佳區塊的快速區塊型動作計測演算法，能夠用於視訊壓縮之上。其主要是利用不同的判斷準則連續消去不可能的區塊而找出在搜尋範圍內最佳的區塊。
首先，我們使用一種類似ARPS的方法找出一個好的起始點，而由這個起始點得到一個比以前方法更好判斷值，使得往後的消去階段能夠更加的有效率。再來，我們依照每個區塊內的特性將區塊分成許多的小區塊，而分割的決定過程是由每個小區塊內的梯度強度總值來決定，梯度強度總值最高的小區塊優先分割，直到每個小區塊內的梯度強度總值都小於一個門檻值，而這一個分割的過程建立了往後連續消去步驟的順序。最後，對於每個區塊都由上述過程得到一個連續消去的順序，並由此順序依次判斷搜尋範圍內的所有可能區塊是否可能為最佳區塊，在每一階段都利用累計性的誤差統計算出現階段的誤差值以減少運算量。在實驗結果中，我們可以看到在不同運動類型的影片中我們所提出來的方法都比以往的方法來的有效率，並且在多參考畫面的動作計測上也有很好的表現。此外，針對特殊的應用，我們所提出的演算法可以很簡單的轉變成為一個近似型動作計測演算法，實驗結果顯示可以用相同的運算量得到比DS更好的還原品質。

In this thesis, we propose a fast and optimal solution for block motion estimation based on an adaptive successive elimination algorithm (SEA). We first apply an fast approximate method likes Adaptive Rood Pattern Search (ARPS) method to obtain a good initial motion vector as well as a tight initial bound of distortion measure to be used in SEA. Then, we apply the multi-level SEA with the elimination order determined by the sum of the gradient magnitudes of each sub-block. Adopt the accumulated distortion method to make the calculation of boundary values at adjacent levels more efficient. Experiments are shown the proposed AdaMSEA algorithm significantly outperforms other previous global motion estimation algorithms, including SEA, MSEA and FGSE, on a wide variety of video sequences. Furthermore, we also demonstrate the superior performance of the proposed AdaMSEA algorithm for multiple-reference-frame motion estimation. For some special application, we can easily modify the proposed AdaMSEA to an approximate motion estimation algorithm to achieve higher PSNR than DS with the same operation counts.

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