在即時視訊傳輸過程中當發生封包遺漏時，會造成連續多張畫面品質下降。許多錯誤隱藏演算法已被提出來，但大多只適用於部分巨方塊遺失，因此本論文提出一個應用於3-D 視訊的高效率全畫面還原演算法。視訊畫面可視為由不同的物件所組成，因此利用擬真動作向量以及3-D視訊中的深度資訊，對各物件求出動作向量。本論文中將動作向量一致並擁有相似深度資訊的像素定義為相同物件，其動作向量稱為以物件為基礎的動作向量，並將參考畫面中的各物件利用其動作向量外插至相對應的位置，藉以重建所遺失的全畫面。但考量到計算複雜度，本論文只對參考畫面中的高頻區域執行以物件為基底的動作向量初估計。由實驗結果顯示，大部分情況下，我們的演算法能提供較佳之視覺品質與較高之信躁比(PSNR)。

In real-time video transmission, the loss of packet results in visual quality degraded for the succeeding frames. Many error concealment algorithms have been proposed to overcome the channel error, but most of them can work in case of losing macro blocks just a part of the full frame. This algorithm proposes an efficient full frame algorithm for depth-based 3-D videos. Each frame can be regarded as combination of objects. True motion estimation (TME) and depth map are exploited to calculate the motion vector (MV) for each object. In this thesis, the object is defined as the pixels with the same MV and similar depth value. We refer to the MV for the object as object-based MV. Each object in reference frame can be extrapolated to reconstruct the damaged frame by the object-based MV. In the consideration of computational complexity, in this method, only the high frequency regions need to execute TME. From the simulation results, our algorithm gives better visual quality and PSNR in most cases.

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