為了加速H.264/進階視訊編碼和可調式視訊編碼之編碼過程，本論文提出了基於時間域與空間域相關性之合併分裂快速模式決策演算法和編碼區塊樣式快速模式決策演算法。而基於時間域與空間域相關性之合併分裂快速模式決策演算法和編碼區塊樣式快速模式決策演算法分別應用於H.264/進階視訊編碼和可調式視訊編碼之中。基於時間域與空間域相關性之合併分裂快速模式決策演算法使用時間域相關性預測每個8×8方塊的動量向量，並且藉由合併分裂過程預測出其餘方塊之動量向量，最後，再藉由空間域相關性取代傳統合併方式以加速16×16方塊的合併過程。編碼區塊樣式是區塊標頭檔中用以標示該區塊是否有殘值資訊的參數。本論文所提出的編碼區塊樣式快速模式決策演算法可藉由可調式視訊編碼增強層中相鄰區塊的編碼區塊樣式值與相對應基礎層的區塊模式，減少增強層目前區塊中需測試的方塊模式。實驗結果顯示本論文所提出的演算法與JM 12.3，JSVM 9.12和其他演算法相比，皆可在PSNR微幅下降與位元率些許上升的情況下，大幅降低編碼端之計算量。

In order to speedup the encoding process of H.264/AVC and Scalable Video Coding (SVC), Temporal and Spatial Correlation-based Merging and Splitting (TSCMS) fast mode decision algorithm and Coded Block Pattern (CBP)-based fast mode decision algorithm are proposed in this thesis. TSCMS and CBP-based fast mode decision algorithms are applied to H.264/AVC and SVC, respectively. In TSCMS, Temporal Correlation (TC) is used to predict the Motion Vectors (MVs) of 8×8 blocks in each macroblock. In addition, the merging and splitting procedure is adopted to predict the motion vectors of other blocks. Afterwards, the spatial correlation is performed to merge 16×16 blocks instead of the conventional merge scheme. CBP value is the syntax used at each Macroblock (MB) header to indicate whether an MB contains residual information or not in CBP-based fast mode decision algorithm. The proposed algorithm can exclude the invalid modes for the mode prediction of the current MB in Enhancement Layer (EL) through the CBP values and MB modes of adjacent MBs in EL and the co-located Base Layer (BL) MB modes. Experimental results show that the proposed algorithms reduce computations significantly with negligible PSNR degradation and bit increase when compared to JM 12.3, JSVM 9.12, and the other existing methods.

CHAPTER 1 Introduction…………………………………………………………...1
1.1 Overview of Video Coding…………………………………………………1
1.2 Overview of H.264/AVC Video Coding Standard……………………...4
1.2.1 Discrete Cosine Transform (DCT) …………………………………...6
1.2.2 Variable Block Size…………………………………………………...8
1.2.3 Multiple Reference Frames…………………………………………10
1.3 Motivation………………………………………………………………...11
1.4 The Organization of the Thesis…………………………………………..13
CHAPTER 2 Overview of Scalable Video Coding and Relevant Work…………..14
2.1 Background of Scalable Video Coding…….……………………………14
2.1.1 Spatial Scalability……………………………...…………………16
2.1.2 Temporal Scalability…………………................…………………17
2.1.3 Quality Scalability……………………………....…………………18
2.2 Inter-layer Prediction……………………….……………………………20
2.2.1 Inter-layer Motion Prediction…………………...…………………20
2.2.2 Inter-layer Intra Prediction…………………………………..……22
2.2.3 Inter-layer Residual Prediction……………………………….……23
2.3 Difference between SVC and H.264/AVC………………...……………24
2.4 Rate-distortion Performance of SVC and H.264/AVC...……………26
2.5 Previous Works in H.264/AVC and Scalable Video Coding.………..……29
2.5.1 Using H.264 Coded Block Patterns for Fast Inter-Mode Selection
[22]………………………………………………………………...30
2.5.2 Layer-Adaptive Mode Decision and Motion Search for Scalable Video Coding with Combined Coarse Granular Scalability (CGS)
and Temporal Scalability [23] ………………………………….…35
CHAPTER 3 Proposed Temporal and Spatial Correlation-based Merging and
Splitting Fast Mode Decision Algorithm in H.264/AVC……............40
3.1 Background of Merging and Splitting Procedure........................................40
3.2 Proposed Algorithm.....................................................................................43
3.2.1 Temporal Correlation……………………………………………43
3.2.2 Spatial Correlation…………………………………………………45
3.2.3 Temporal and Spatial Correlation-based Merging and Splitting
Fast Mode Decision Algorithm……………………………………47
CHAPTER 4 Proposed CBP-based Fast Mode Decision Algorithm in SVC...........50
4.1 The Analysis of CBP Characteristics.........................................................50
4.2 Analysis of the Largest Temporal Level Information................................54
4.3 CBP-based Fast Mode Decision Algorithm……........................................56
4.3.1 CBP-based Fast Mode Decision…………………………………56
4.3.2 Temporal Relativity Mode Selection Method……………………59
CHAPTER 5 Experimental Results……………….……………………………….61
5.1 Testing Platform of Experimental Results.……………...................61
5.2 Objective Measurement………………….…………………...................63
5.3 Experimental Results of TSCMS Fast Mode Decision Algorithm……....65
5.4 Experimental Results of CBP-Based Fast Mode Decision Algorithm…....69
5.4.1 Simulation Results of 2-layer SVC……..…...……………………70
5.4.2 Simulation Results of 4-layer SVC…..……...……………………86
CHAPTER 6 Conclusions and Future Work………………………………………95
6.1 Conclusions…………………………………………………………….....95
6.2 Future Work……………………………………………………………….98
Bibliography………………………………………………………………………….99
Curriculum Vitae………………………………………………………………….104
Publications……………………………….......…………………………………….105

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