Zhang和Zafar提出基於小波轉換與多重解析度的運動估測(MRME)視訊壓縮演算法。與傳統的方塊比對演算法(BMA)相比，該演算法減少計算量且能較有效地表示訊號。MRME使用完全搜尋演算法(FSA)搜尋子頻帶影像的運動向量，但其仍可能找不到適合的運動向量。Jinwen Zan提出一新的MRME演算法，其使用一臨界值法丟棄不適合的匹配方塊。但是只在最低解析度的子頻帶影像中作運動估測，因此重建影像品質下降。

本論文結合上述兩種演算法的優點，使用一修改型二步驟臨界值法搜尋運動向量。在步驟一，第一個臨界值決定運動向量是否需要進一步更新。在步驟二，第二個臨界值決定匹配方塊是否適合，若不適合，方塊標記為intracoded。本演算法對於影像中不同運動複雜度的方塊，使用不同程度的計算量，因此節省運動估測的計算量。此外，丟棄不適合的匹塊方塊提升運動估測的效能。

實驗結果顯示，在幾種不同運動特性的視訊序列中，我們的演算法比MRME及新的MRME提高了0.2 - 0.7 dB 的影像品質。新的MRME的執行時間比MRME少了78%，而我們演算法的執行時間比MRME少了20% - 50%。

A video compression algorithm based on the wavelet transform and multiresolution motion estimation (MRME) was proposed by Zhang and Zafar. As compared to conventional block matching algorithm, it reduces the computational load and exploits the efficient representation provided by the wavelet transform. In the MRME technique, the full search algorithm (FSA) is used to search the correct motion vectors in subimages. However, it is could not always find a good match. A new MRME has been proposed by Jinwen Zan. In the technique, the mismatched blocks are discarded by a thresholding. But the motion estimation is only implemented in the lowest resolution subimage. This results the deterioration of the quality of the reconstructed images.

We take the advantages of both of the above algorithms. We modify the new MRME technique by using a two-step thresholding for estimating the motion vectors. In the first step, the first threshold is used to determine if the motion vectors need to be refined with further searching. In the second step, if the dissimilarity between the block and the matched block is greater than the second threshold, the block is labeled as intracoded. Via the proposed algorithm, different computational efforts can be made for regions having different complexity of motion and thus saving computation of motion estimation. Besides, the mismatched block can be discarded and thus improving the motion estimation performance.

The experiments have shown better results from several test video sequences with various motion characteristics. The proposed algorithm gives a PSNR of about 0.2 - 0.7 dB better than either MRME or new MRME. The processing time of new MRME is about 78% less than that of MRME. And the processing time of the proposed algorithm is about 20% - 50% less than that of MRME.

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