在多視角視訊壓縮技術(MVC)中，每個畫面群(GOP)中關鍵畫面的編碼品質將會影響到整個畫面群壓縮的效能，因此關鍵畫面的編碼是一個重要的研究議題。當考量到不同攝影機間的品質差異時，本論文提出了一個關鍵畫面的動態預測結構來改善因某些損壞的攝影機所造成的錯誤傳遞問題。在編碼多視角視訊前，我們會檢查每個視角第一個GOP中的關鍵畫面，並判斷其是否為損壞攝影機的視角，然後重新排列視角間的編碼順序，以提高編碼後的影像品質。重新排列後的預測結構將用於所有的GOP編碼並可獲得可觀的編碼增益。基於四個多視角視訊的測試影片，且假設相機的損壞不超過一半的條件下，實驗結果證實新的動態預測架構在壓縮比上的表現上優於MVC的固定編碼結構。

In the multi-view video coding (MVC), the encoding of key picture in each group of picture (GOP) is crucial since the compression performance of each GOP depends on the quality of the encoded key picture. Taking the quality conditions of cameras into account, this thesis presented an adaptive inter-view prediction structure of key pictures to alleviate the error propagation problem caused by some poor cameras. Before encoding multi-view sequences, we examine the key picture in the first GOP of each view to identify these poor cameras, and then adjust the inter-view prediction structure for key pictures, i.e. re-arrange the order intra-coded and inter-coded pictures, to enhance the coded image quality. The rearranged inter-view prediction structure is used to encode key pictures of all GOPs to achieve a substantial coding gain. Based on four test multi-view sequences with the assumption that no more than half of cameras are malfunctioned, experimental results demonstrate the bit rate superiority of the proposed adaptive prediction structure when compared to the traditional MVC with fixed structure of the key pictures.

論文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第 一 章 緒論 1
1.1 前言 1
1.2 研究動機與目的 2
第 二 章 多視角視訊壓縮系統 4
2.1 多視角視訊壓縮系統之預測 4
2.2 多視角視訊壓縮系統之架構演進 5
第 三 章 動態關鍵畫面與動態DIBR技術 10
3.1 動態關鍵畫面 10
3.2 DIBR技術 21
第 四 章 實驗結果 25
4.1 動態關鍵畫面架構 25
4.2 DIBR技術 29
第 五 章 結論 31
參考文獻 32

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