畫面頻率提升演算法是一種提高低畫面頻率影片的視覺品質的技術。在大多數的畫面頻率提升演算法中，使用以區塊為單位的動作向量預估方式，而容易產生方塊效應，影響畫面的視覺品質。在這篇論文中，我們提出了一個基於立體影片的深度資訊，改良擬真動作預估來提升畫面頻率。在我們提出的動作預估演算法中，由於深度資訊區分為不同資訊的動作向量會被分開更新，亦即在不同物件的像素會被給予不同的動作向量。因此我們可以得到以物件為單位且更為準確的動作向量，使得畫面頻率提升的品質更佳。而對於重複覆蓋的區域，也能以深度資訊來從候選動作向量中選取最適當的動作向量。實驗結果顯示，在主觀和客觀上，即便是在較為複雜和有較快動作的場景，我們的演算法都可以有效改良畫面頻率提升後的影片品質。

Frame rate up-conversion (FRUC) is a visual quality enhancement technology for the low frame rate video. Most FRUC algorithms use the block-based motion estimation and it may cause blocking artifacts which will affect the quality of the video. In this thesis, we present an improved multi-pass true motion estimation algorithm based on 3D videos for FRUC. In the proposed algorithm, the motion vectors of different objects which are parted with the depth information of 3D videos will be refined independently. Pixels in different objects will be referred to diverse motion vectors. Therefore, more accurate object–based motion vectors can be obtained which causes the performance of FRUC. For overlapped regions, the motion vector will be chosen in candidates by depth information. Experimental results show that the proposed algorithm effectively enhances the overall quality of the frame rate up-converted video sequence, both subjectively and objectively, even with complex scenes and fast motion.

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