基於空間特徵與摺積神經網路於 H.266/FVC 視訊畫面內編碼快速分割決策

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研究生：	涂育良 Yu-Liang Tu
論文名稱：	基於空間特徵與摺積神經網路於 H.266/FVC 視訊畫面內編碼快速分割決策 Fast Partition Decision for H.266/FVC Video Intra Coding Based on Spatial Features and CNNs
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：	中文
論文頁數：	82
中文關鍵詞：	未來視訊壓縮編碼、編碼單位、畫面內編碼、分區預測、摺積神經網路、空間特徵
外文關鍵詞：	Future Video Coding (FVC), Coding Unit (CU), Intra Coding, Partition Prediction, Convolutional Neural Network (CNN), Spatial Feature
相關次數：	點閱：26 下載：0
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跟隨著網路和多媒體的科技迅速成長，我們人類平日生活的高解析度視訊重要性一日千里，最近市面已出現許多 4K 高解析度的視訊，可以看見未來高解析度視頻勢必會成為主流，而目前的最新視頻壓縮標準 H.265/HEVC 已經逐漸不夠能用，因此ITU-T VCEG 和 ISO/IEC MPEG 共同組成的JVET (Joint Video Exploration Team) 來制定新一代的視訊壓縮標準 H.266/FVC (Future Video Coding)，從2015年開始討論，且預計2021年正式發佈變國際視訊壓縮標準。
H.266/FVC與H.265/HEVC相比不僅採用了QT(QuadTree)且還新增了BT(Binary Tree)，不但將複雜的CU、PU和TU的組成元素除去，且能夠支援最大256x256到最小8x8的正方形區塊，以依據更多不同大小畫面的紋理特性編碼。QTBT的架構雖比QT提供更好的編碼效能，但其預測數量的增加造成在執行畫面編碼時間提高了5.6倍，於是針對畫面內編碼，如何發展增快CU編碼時間下的決策，這是非常重要的議題。
本論文結合近年十分熱門的人工智慧系統 (Artificial Intelligence, AI)，提出基於空間特徵與摺積神經網路於 H.266/FVC視訊畫面內編碼快速分割決策。主要分為兩部分探討：首先在第一部份如何使用空間特徵來分析紋理特徵，以來決策是否切割與減少CNN使用次數；在第二部份則是針對預測模型的訓練和訓練資料的選擇來討論，再將訓練好的預測模型結合至 H.266/FVC 壓縮參考軟體當中來執行編碼。

With the rapid growth of Internet and multimedia technology, the high-resolution video of our daily lives is becoming important. Recently, there have been many 4K high-resolution videos on the market. It can be expected that high-resolution video will become the mainstream in the future. However, the video compression standard H.265/HEVC has gradually become insufficient. Therefore, the JVET (Joint Video Exploration Team) consist of ITU-T VCEG and ISO/IEC MPEG has developed a new generation of the video compression standard H.266/ FVC (Future Video Coding). JVET is expected to be officially released in 2021 to become an international video compression standard. H.266/FVC is different from H.265/HEVC, it uses QT (QuadTree), adds QTBT (QuadTree plus Binary Tree), and removes the complex elements of CU, PU, and TU. H.266/FVC codes according to the texture characteristics of more pictures of different sizes. Although the QTBT architecture provides better coding performance than QT. Therefore, for intra-picture coding, how to develop decision-making under CU coding time is very important.
This thesis uses the Artificial Intelligence (AI) system, and proposes a fast partition prediction and decision-making method based on the convolutional neural network in the H.266/FVC video coding frame. The discussion is mainly divided into two parts: First, using spatial features to analyze texture features is to decide whether to cut and reduce the number of CNN usage. Secondly, discussing the training of the prediction model and the selection of training data, and then combine the trained prediction model with H.266/FVC to perform encoding.

摘要 .................................................................................................................... I
ABSTRACT...................................................................................................... II
誌謝 ................................................................................................................. III
目錄 ................................................................................................................. IV
附圖索引 ........................................................................................................ VII
附表索引 ......................................................................................................... IX
第一章 緒論 ..................................................................................................... 1
1 研究背景 ................................................................................................. 1
2 研究動機與目的 ..................................................................................... 2
3 論文架構 ................................................................................................. 3
第二章 H.266/FVC 視訊編碼標準介紹 .......................................................... 4
1 H.266/FVC 視訊編碼介紹 ...................................................................... 4
1.1 H.266/FVC 與 H.265/HEVC 差異 ................................................. 4
1.2 編碼流程介紹 .................................................................................. 6
2 H.266/FVC 視訊編碼架構介紹............................................................... 7
2.1 編碼單元 (Coding Unit, CU) ........................................................... 7
2.2 預測單元 (Prediction Unit, PU)..................................................... 13
2.2.1 畫面內預測 (Intra prediction) ................................................. 13
2.2.2 畫面間預測 (Inter prediction) ................................................. 16
2.3 轉換單元 (Transform Unit, TU) .................................................... 21
3 H.266/FVC 環境設定及視訊樣本介紹 ................................................. 22
3.1 環境設定 ........................................................................................ 22
3.2 視訊樣本介紹 ................................................................................ 24
第三章 深度學習介紹 .................................................................................... 29
1類神經網路 ............................................................................................ 30
1.1 類神經網路的發展歷史................................................................. 30
1.2 倒傳遞神經網路及多層感知機 ..................................................... 33
2 深度學習 ............................................................................................... 36
2.1 摺積神經網路(Convolutional Neural Network) ............................. 36
3 基於深度學習的切割模式決策相關文獻............................................. 40
第四章 基於空間特徵與摺積神經網路於 H.266/FVC視訊畫面內編碼快速分割決策 ......................................................................................................... 42
1 四分樹分割(QT DEPTH0和QT DEPTH1) .............................................. 42
1.1 四分樹(QTDepth0)特徵提取 ......................................................... 42
1.2 四分樹(QTDepth1)特徵提取 ......................................................... 44
1.2.1 QP在MeanVarianceGRAD的影響 ........................................... 45
1.3 四分樹快速決策與閥值設定 ......................................................... 46
2 CNN於四分樹(QTDEPTH2)快速分割決策 ........................................... 48
2.1 前處理部分 (Pre-processing section) ............................................ 48
2.1.1 根據CNN的快速決策 ........................................................... 51
2.2 訓練部分 (Training section) .......................................................... 51
2.3 驗證階段 (Validation stage) .......................................................... 54
3 提出方法的整體流程 ............................................................................ 56
第五章 實驗結果與分析討論 ........................................................................ 58
1 實驗環境的設置 ................................................................................... 58
2 實驗結果 ............................................................................................... 60
第六章 結論與未來展望 ................................................................................ 63
參考文獻 ......................................................................................................... 64
                                

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