一種結合支持向量機與卷積神經網路的架構以降低HEVC計算複雜度之研究

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姓名

王致傑(Jie-Jay Wang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

一種結合支持向量機與卷積神經網路的架構以降低HEVC計算複雜度之研究
(Computation Reduction of HEVC Intra Prediction using combined SVM and CNN)

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摘要(中)

隨著科技的高速發展與使用者越來越多的需求，高解析度的影像逐漸充斥了人們的生活。為了能夠更高效率的壓縮這些巨大的視頻資料量，HEVC採用了一些更新穎的技術，如編碼樹單元、碼率失真最佳化等等，但於此同時也造成了編碼計算複雜度的提升。本論文結合近幾年來十分熱門的深度學習與機器學習，即卷積神經網路與支持向量機，將其應用於HEVC編碼單元深度決策。不同於原始HEVC遞迴運算編碼單元深度0至3，本論文在編碼一開始時先使用支持向量機將編碼單元分成單調區塊與複雜區塊，再利用卷積神經網路分層向下細分。分類完成的區塊將只會進行特定深度的編碼並提前終止後續的編碼計算，藉此節省編碼其他深度所需的運算時間。而後進一步將支持向量機的結果導入卷積神經網路模型，設計一個映射函數使其修正模型的預測判斷。最終實驗結果顯示，與HEVC相比，整體平均BDBR上升0.66%的情況下，編碼時間大約可以節省49%。

摘要(英)

With the rapid development of technology and the increasing requirements of users, high-resolution images are gradually filling our lives. In order to compress huge amounts of video data more efficiently, HEVC utilizes some newer technologies, such as coding tree units (CTU), rate distortion optimization (RDO), etc., but it also increases a lot of computation complexity at the same time. In this thesis, we combine the deep learning (DL) which is popular in recent years and the machine learning (ML), scilicet convolutional neural network (CNN) and support vector machine (SVM), applying them to the depth decision of coding units in HEVC. Different from the original HEVC which computes the depth of coding units 0 to 3 recursively, we first divide CTU into homogeneous blocks and complex blocks with SVM, and then classifying them hieratically by CNN models. The classified blocks will only encode at some specific depths and terminate calculations of encoding in advance, thus saving the computation time of other encoding depths. After that, the results of SVM are imported into CNN models, and some mapping functions are designed to modify the prediction of these models. The final experimental results in this thesis show that the overall average BDBR rises by 0.66%, and the encoding time can be saved by 49%.

關鍵字(中)

★ 高效率視頻編碼
★ 畫面內預測
★ 支持向量機
★ 卷積神經網路
★ 碼率失真最佳化
★ 編碼單元
★ 快速深度決策

關鍵字(英)

★ HEVC
★ Intra Prediction
★ SVM
★ CNN
★ RDO
★ CU
★ Fast Depth Decision

論文目次

目錄
第一章、緒論 1
1.1 高效率視頻編碼(High Efficiency Video Coding)標準介紹 1
1.2 HEVC編碼架構介紹 2
1.2.1 HEVC架構 2
1.2.2 碼率失真代價函數 3
1.2.3 編碼單元(Coding Unit) 5
1.2.4 預測單元(Prediction Unit) 6
1.2.5 量化參數(Quantization Parameter) 7
1.3 支持向量機(Support Vector Machine)介紹 9
1.3.1 機器學習(Machine Learning) 9
1.3.2 支持向量機介紹 10
1.4 卷積神經網路(Convolutional Neural Network)介紹 13
1.4.1 深度學習之類神經網路 14
1.4.2 倒傳遞神經網路演算法 16
1.4.3 深度神經網路(Deep Neural Network)介紹 18
1.4.4 卷積神經網路(Convolutional Neural Network)介紹 19
1.5 深度學習框架TensorFlow介紹 23
1.5.1 深度學習框架介紹 23
1.5.2 TensorFlow優缺點 25
1.5.3 TensorRT 26
1.6 研究動機與目的 27
1.7 論文架構 27
第二章、相關文獻回顧 28
2.1 減少CU編碼複雜度相關文獻回顧 28
2.1.1 利用紋理特徵減少CU編碼複雜度相關文獻回顧 28
2.2 利用SVM減少CU編碼複雜度相關文獻回顧 36
2.2.1 Computational Complexity Reduction for HEVC Intra Prediction with SVM 36
2.3 利用CNN減少CU編碼複雜度相關文獻回顧 49
2.3.1 A Deep Convolutional Neural Network Approach for Complexity Reduction on Intra-Mode HEVC 49
2.3.2 Asymmetric-Kernel CNN Based Fast CTU Partition for HEVC Intra Coding 55
第三章、結合SVM與CNN應用於編碼區塊快速深度決策演算法 62
3.1 整體系統架構 62
3.1.1 前處理階段 63
3.1.2 訓練階段 65
3.1.3 測試階段 71
3.2 快速深度決策演算法 80
3.2.1 快速深度決策演算法流程 80
3.2.2 效能分析 82
3.3 性能探討 88
3.3.1 卷積神經網路模型性能討論 88
3.3.2 總體模型性能比較 91
第四章、結合學習進階探討 95
4.1 結合學習 95
4.1.1 卷積神經網路與支持向量機之特徵分析 95
4.1.2 深度學習與結合學習之比較 98
4.2 可調式決策閾值 106
4.2.1 閾值曲線函數優化 106
4.2.2 可調式閾值效能分析 112
第五章、結論與未來展望 117
參考文獻 118

參考文獻

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指導教授

林銀議(Yin-Yi Lin)

審核日期

2020-1-17

推文