三維高效率視訊編碼(3D High Efficiency Video Coding，3D-HEVC) 是多視角視訊最新的3D視訊編碼標準。3D-HEVC除了利用時間域和空間域相關性外，還加入了視角域做為參考，也因此導致3D-HEVC編碼時間和複雜度明顯的增加。為了降低編碼複雜度，本論文利用Sobel邊緣偵測演算法，以景深畫面的梯度值為依據，將編碼樹單位分類成不同的種類，並依照分類提出編碼單位的快速深度決策演算法與調整移動估計搜尋範圍；另外，本論文根據統計與參考空間域和視角域之鄰近編碼樹單位的相關性提出預測單位快速模式決策演算法。實驗結果顯示本論文所提的演算法相較於參考文獻能減少更多的編碼時間，且維持良好的視訊品質與位元率。

3D-HEVC(The 3D Extension of High Efficiency Vide Coding) is the latest 3D video coding standard with the video format of multi-view plus format. 3D-HEVC utilizes not only the information of temporal and spatial domains but also that of inter-view domain. Thus, the time consumption and complexity of 3D-HEVC also increase significantly. In order to reduce the coding complexity of depth map, this thesis uses Sobel edge detection algorithm to calculate the gradient value of the depth map. We divide the coding tree unit (CTU) into different types according to the gradient. According to the type of CTU, the fast depth decision of the coding unit is proposed and the search range is adjusted adaptively. In addition, the statistical results and the correlations between neighboring CTUs in spatial domain and inter-view domain are also utilized to propose the fast mode decision of the prediction unit. Experimental results show that the algorithm proposed in this thesis can reduce more coding time than those of the previous works, and maintain good video quality and bit-rate.

摘要 I
Abstract II
目錄 III
圖目錄 VI
表目錄 IX
第一章 緒論 1
1.1 三維視訊編碼簡介 1
1.2 三維視訊編碼架構 6
1.2.1 編碼單位(Coding Unit，CU) 7
1.2.2 預測單位(Prediction Unit，PU) 9
1.2.3 轉換單位(Transform Unit，TU) 12
1.3 3D-HEVC畫面內預測(Intra Prediction) 12
1.4 3D-HEVC 畫面間預測(Inter Prediction) 13
1.4.1 移動估計(Motion Estimation) 13
1.4.2 Advanced Motion Vector Prediction(AMVP) 14
1.4.3 Disparity Vector From Neighbor Blocks (NBDV) 15
1.5 研究動機 17
1.6 論文架構 18
第二章 文獻探討 19
2.1 應用於景深畫面之快速演算法 19
2.2 應用於彩色畫面之快速演算法 20
2.3 應用於彩色與景深之快速演算法 22
第三章 提出的景深圖快速編碼演算法 25
3.1 編碼單位深度快速決策 26
3.1.1 編碼單位大小與邊緣強度分析 26
3.1.2 編碼樹單位之分類 38
3.1.3 編碼單位深度快速決策演算法 52
3.2 基於編碼樹單位種類之搜尋範圍調整 55
3.3 預測單位模式快速決策 56
3.3.1 預測單位模式與CTU種類分析 56
3.3.2 預測單位模式快速決策演算法 60
第四章 實驗結果 63
4.1 實驗環境介紹 63
4.2 實驗結果 69
第五章 結論與未來展望 81
參考文獻 83

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