Mode Decision using Inter-view Dependencies and Depth Information for Multiview Video Coding

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研究生：	徐君潔 Hsu, Chun-Chieh
論文名稱：	Mode Decision using Inter-view Dependencies and Depth Information for Multiview Video Coding 運用深度資訊之快速多視角視訊編碼技術研究
指導教授：	王家祥 Wang, Jia-Shung
口試委員:	杭學鳴潘晴財
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2011
畢業學年度：	99
語文別：	中文
論文頁數：	55
中文關鍵詞：	多視角編碼、快速運動向量補償、快速視差向量補償、加速模式選擇、深度資訊
外文關鍵詞：	multiview video coding, fast motion estimation, fast disparity estimation, fast mode decision, depth information
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多視角編碼技術使用了運動向量補償(motion estimation)和視差向量補償(disparity estimation)去發掘不同視角影片之間的相關性且利用編碼率失真最佳化的區塊模式選擇(rate-distortion optimization)達到極高的壓縮效果，然而也造成了編碼器的計算量大幅提升。為了降低計算量，許多論文將注意力放在快速運動向量補償演算法、快速視差向量補償演算法或快速模式選擇演算法，但是卻沒有完整的利用鄰近的視角影片壓縮過後提供的訊息。
本篇論文利用不同視角影片彼此的相關性和深度資訊提出了一個快速模式選擇演算法。首先根據鄰近視角影片壓縮時選擇的模式，我們致力於減少每個宏塊(macroblock)需要計算的模式數量，接著結合以臨界值為基礎的提早結束模式選擇機制來進一步的加速模式選擇的流程。第二部分，我們利用運動向量補償的結果去決定是否需要計算視差向量補償。第三部分，我們分別減少運動向量補償和視差向量補償的搜尋範圍。最後，如果此多視角影片含有深度資訊，我們會利用此深度資訊去進一步的縮小視差向量補償的搜尋範圍和過濾掉不適合的參考宏塊。實驗結果顯示，本篇論文可以加速約80%的編碼時間且只造成少部分的失真率下降以及編碼率提高。

Multiview Video Coding (MVC) performs both disparity and motion estimation to exploit inter-view correlations along with the rate distortion optimization to achieve superior coding efficiency, however the encoder load dramatically increases as well. To reduce the stunning computational complexity, many research works focus on fast motion estimation, fast disparity estimation and/or fast mode decision, but are not fully utilized the information supplied by inter-view referencing frames. In this thesis, a fast mode decision algorithm using both inter-view dependencies and depth information is proposed. First, we delicately arrange the minimal candidate modes for each macroblock according to the valued modes corresponding to its inter-view referencing macroblocks, along with the threshold-based early termination to end up the mode decision procedure earlier. Next, we use the result of motion estimation to decide if disparity estimation is required. Third, the search ranges of motion estimation and disparity estimation will be reduced accordingly. Finally, apply the depth information (if available) to refine the search range of disparity estimation and filter out the inappropriate referencing macroblocks as well. The experimental results demonstrate that the proposed algorithm reduces around 80% of the entire encoding time with negligible PSNR drop and bit-rate increasing.

致謝    I
中文摘要    II
Abstract    III
Table of Contents    IV
List of Figures    VI
List of Tables    VIII
Chapter 1. Introduction    1
Chapter 2. Related Works    5
2-1. Background Knowledge    5
2-1-1. Multiview Video Coding (MVC) Prediction Structure    5
2-1-2. Global Disparity Vector (GDV)    6
2-1-3. Overview of MVC Mode Decision    8
2-1-4. Color plus Depth Map Video    12
2-2. Fast Algorithms in MVC    14
2-2-1. Fast ME and DE Algorithms    14
2-2-2. Fast Mode Decision Algorithms    16
2-2-3. Depth-information-based Algorithms    17
Chapter 3. Fast Mode Decision Algorithm    18
3-1.    GDV-Based Candidate Modes Selection    23
3-2.    Threshold-Based Early Termination    25
3-3. Reduce Disparity Estimation    28
3-4. Dynamic Search Range Adjustment    29
3-4-1. Motion Estimation Search Range Prediction    29
3-4-2. Disparity Estimation Search Range Prediction    30
3-5. Use Depth Information    34
3-5-1. Relation between Depth and Disparity    34
3-5-2. Disparity Estimation Search Range Refinement    35
3-5-3. Filter Out Unsuitable Referencing MBs    37
Chapter 4. Experimental Results and Discussion    39
4-1. Encoding Time Evaluation/    40
4-2. Comparing with other works    49
Chapter 5. Conclusions and Future Works    51
References    53

                                

[1] ISO/IEC/JTC1/SC29/WG11, “Multiview Coding Using AVC,” Bangkok, Thailand, Jan. 2006.
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