於VVC視訊編碼畫面內針對編碼單元劃分模式之快速演算法

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研究生：	劉邦浩 Bang-Hao Liu
論文名稱：	於VVC視訊編碼畫面內針對編碼單元劃分模式之快速演算法 Fast CU Partitioning Algorithm for VVC Intra Coding
指導教授：	張寶基 Pao-Chi Chang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 通訊工程學系 Department of Communication Engineering
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	84
中文關鍵詞：	多功能視訊編碼、編碼單位、快速演算法、畫面內編碼、特徵轉換、特徵分析、摺積神經網路模型
外文關鍵詞：	Versatile Video Coding (VVC), Coding Unit (CU), Fast Algorithm, Intra Coding, feature analysis, convolutional neural network model(CNN), feature conversion
相關次數：	點閱：15 下載：0
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自2015年開始JVET (Joint Video Exploration Team)開討論起最新一代的視訊壓縮標準H.266/VVC討論最新的視訊壓縮標準H.266/VVC (Versatile Video Coding)。相較於前一代標準採用了QTMT (Quad Tree with nested Multi-type Tree coding block structure)的CU編碼結構。其支援最大128×128至最小4×4的方形以及矩形編碼區塊。該種結構能較好對視訊紋理做細分，提升編碼品質，但如此複雜的結構也將伴隨大量的演算法耗時，所以如何使用快速演算法使編碼品質和耗時達成平衡將是本論文的目標。
本論文提出基於特徵分析的QTMT快速演算法，該算法能分別減少二分樹劃分和三分樹劃分內可被利用的模式，其中二分樹與三分樹劃分的判斷結構皆相同，該演算法分為3部分，特徵圖建立與分析、傳統的分類方式與神經網路模型分的建立。首先，建立基於QTMT單位分區的特徵圖，並且利用該特徵圖生成編碼分區的特徵資料組。然後傳統的分析方法找出最佳的判斷式，將有著顯著資料的特徵組進行判斷。如果該特徵組有著細微變化，則特徵圖會進入提出的摺積神經網路模型進行分類。

Since 2015, JVET (Joint Video Exploration Team) has started to discuss the latest video compression standard H.266/VVC (Versatile Video Coding). Compared with the previous generation standard, the CU coding structure of QTMT (Quad Tree with nested Multi-type Tree coding block structure) is adopted. It supports square and rectangular coded blocks from a maximum of 128×128 to a minimum of 4×4. This structure can better subdivide the video texture and improve the coding quality, but such a complex structure will also be accompanied by a lot of time-consuming algorithms, so how to use fast algorithms to balance the coding quality and time-consuming will be the focus of this paper the goal.
This paper proposes a fast MT algorithm based on feature analysis, which can reduce the modes that can be used in BT partition and TT partition respectively. Among them, the judgment structure of binary tree and tri-tree partition is the same. There are three parts, the establishment and analysis of feature maps, the establishment of traditional classification methods and neural network models. First, a feature map based on the MT unit partition is established, and the feature map is used to generate a feature data set of the coding partition. Then, the traditional analysis method is used to find the best judgment formula, and judges the characteristic group with significant data. If the feature group has slight changes, the feature map will enter the proposed convolutional neural network model for classification.

摘要................................... VI 
致謝................................... IX 
目錄................................... X 
附圖索引 .............................. XIII 
附表索引 .............................. XV 
第一章 緒論 ........................... 1 
1 研究背景 .......................... 1 
2 研究動機與目的 ..................... 1 
3 論文架構 .......................... 2 
第二章 H.266/VVC 視訊編碼標準介紹 .................. 3 
1 H.266/VVC 視訊編碼介紹 ........................ 3 
1.1 H.266/VVC與 H.265/HEVC差異 ................. 3 
1.2 編碼流程介紹................................. 4 
2 H.266/VVC 視訊編碼架構介紹 .................... 5 
2.1 編碼單元 (Coding Unit, CU) ................. 5 
2.2.1 編碼樹的劃分結構 .......................... 5 
2.2.2 針對圖像邊緣性質的劃分標準 ................. 8 
2.2.3 對於冗餘編碼單元的劃分限制 ................. 9 
2.2.4 虛擬管道數據單元 .......................... 10 
2.2 預測單元 (Prediction Unit, PU) ............. 11 
2.2.1 畫面內角度預測 ............................ 12 
2.2.2 多參考線預測模式 .......................... 14 
2.2.3 畫面內子區塊劃分模式 ...................... 16 
2.3 轉換單元 (Transform Unit, TU) .............. 17 
3 H.266/VVC 環境設定及視訊樣本介紹 .............. 19 
3.1 環境設定 ................................... 19 
3.1.1 All-Intra (AI) .......................... 20 
3.1.2 Low-Delay (LD) .......................... 21 
3.1.3 Random-Access (RA) ...................... 21 
3.2 視訊樣本介紹 ............................... 22 
第三章 H.266/VVC 畫面內之快速演算法相關研究介 ...... 24 
1 基於變異數和梯度數值的傳統決策 ................. 24 
2 基於低複雜度 CTU 結構的改善方案 ................ 25 
3 基於大量特徵分析的輕量化神經網路模型 ............ 27 
第四章 針對畫面內編碼單元劃分模式之快速演算法 ....... 31 
1 快速演算法設計概要(DIAGRAM DESIGN OF FAST ALOGORITHM) ....................................................... 31 
2 最佳 CU 大小的選擇(SELECTION OF OPTIMAL CU SIZE) .... 34 
3 二分樹與三分樹劃分的決策(BINARY AND TENARY TREE DECISION) ....................................................... 36 
3.1 基於單位劃分區塊的特徵轉換 ......................... 38  
3.2 決策制定與分類機制 ................................ 41 
3.2.1 特徵圖與劃分模式間關係之建立 ..................... 41 
3.2.2 特徵圖與最佳劃分模式之分析 ....................... 44 
3.2.3 最佳的劃分模式之決策 ............................ 52 
4 提出的摺積神經網路(PROPOSED CONVOLUTION NEURAL NETWORK) ....................................................... 54 
4.1 網路模型之系統架構與設計 .......................... 54 
4.2 訓練階段與資料之建置 .............................. 58 
第五章 實驗結果與分析討論 ............................... 59 
1 實驗環境設置 ....................................... 59 
2 實驗結果 ........................................... 60 
第六章 結論與未來展望 ................................... 65 
參考文獻 ............................................... 66 
                                

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