利用支持向量機結合卷積神經網路降低HEVC畫面間預測之計算複雜度研究

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鍾聖政(Sheng-Cheng Chung) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

利用支持向量機結合卷積神經網路降低HEVC畫面間預測之計算複雜度研究
(Computation Reduction of HEVC Inter Prediction using combined SVM and CNN)

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至系統瀏覽論文 (2024-8-6以後開放)

摘要(中)

在這網路快速進步的時代，對於高解析度影像的需求不斷提升，高解析度代表著資料量相對龐大，HEVC/H.265採用編碼單元(Coding Unit,CU)、預測單元(Prediction Unit,PU)、碼率失真最佳化(Rate-Distortion Optimization)等等，這些先進的編碼技術提高了壓縮率，但運算複雜度卻也大幅的增加，本論文結合卷積神經網路與支持向量機應用於編碼單元深度決策。首先在編碼一開始使用支持向量機將編碼單元分類為只做深度0、深度0~1、深度0~2、深度0~3四種類別，再各別使用卷積神經網路依據在支持向量機已取得的畫面間預測移動向量值做為特徵(Feature)，判斷是否需要提前終止，提前終止的區塊只會進行一次深度的編碼，且因為移動向量值為特徵複用，進而節省編碼所需花費的運算時間。在只進行64x64編碼決策的情況下，實驗結果與HEVC進行比較，平均BDBR上升1.32%的情況下，編碼時間節省46.84%。

摘要(英)

In the era of rapid Internet advancement, the demand for high-resolution images continues to increase. The use of high-resolution images implies that a large amount of data is resulted. HEVC/H.265 adopts advanced encoding techniques such as Coding Unit (CU), Prediction Unit (PU), and Rate-Distortion Optimization to improve the compression ratio of data; however, such approach also increases the computational complexity significantly. In this thesis, Convolutional Neural Network (CNN) was combined with Support Vector Machine (SVM) and applied to the depth decision of coding unit. At the beginning of the coding process, Support Vector Machine was used to sort the coding units into four categories of depth 0, depth 0~1, depth 0~2 and depth 0~3. Convolutional Neural Network was then used to determine whether early termination is needed based on the inter prediction motion vector value obtained by the Support Vector Machine as a feature. The block that terminates early will only be deep-coded once. Since the motion vector value is feature multiplex, it reduces the computation time required for coding. For 64x64 coding decision, the experimental results were compared with HEVC, showing that the coding time was reduced by 46.84% when the average BDBR was increased by 1.32%.

關鍵字(中)

★ 高效率視頻編碼
★ 支持向量機
★ 卷積神經網路
★ 編碼單元
★ 快速深度決策
★ 畫面間預測
★ 改善編碼性能
★ 深度學習
★ 移動向量

關鍵字(英)

★ High Efficiency Video Coding (HEVC)
★ Support Vector Machine(SVM)
★ Convolutional Neural Network(CNN)
★ Coding Unit(CU)
★ Inter Prediction
★ Improved Coding Performance
★ Deep Learning
★ Motion Vector
★ Fast Depth Decision

論文目次

第一章、緒論 1
1.1研究動機與目的 1
1.2論文架構 2
第二章、H.265/HEVC視訊編碼標準介紹 3
2.1 H.265/HEVC視訊編碼介紹 3
2.2 H.265/HEVC視訊編碼架構介紹 4
2.2.1 編碼單元(Coding Unit, CU) 5
2.2.2 預測單元(Prediction Unit, PU) 7
2.2.3 轉換單元(Transform Unit, TU) 8
2.2.4 畫面間預測(Inter Prediction) 8
第三章、支持向量機及深度學習介紹 17
3.1支持向量機(Support Vector Machine, SVM) 17
3.2深度學習介紹 19
3.2.1 類神經網路 20
3.2.2 深度神經網路(Deep Neural Networks, DNN) 20
3.2.3 卷積神經網路(Convolutional Neural Networks, CNN) 21
第四章、相關文獻回顧 25
4.1 利用支持向量機減少編碼單元複雜度相關文獻回顧 25
4.1.1 Reduction of Computational Complexity HEVC Inter Prediction With Support Vector Machine 25
4.2利用CNN減少CU編碼複雜度相關文獻回顧 31
4.2.1 Fast CU Depth Decision for HEVC Using Neural Networks 32
4.2.2 SVM/CNN-based CTU partition for HEVC inter prediction 34
第五章、結合SVM與CNN應用於編碼單元快速決策演算法 37
5.1 編碼單元快速決策演算法 37
5.1.1 演算法優缺點探討 37
5.1.2 編碼單元快速決策演算法流程 38
5.2 整體系統架構 40
5.2.1 前處理階段(Pre-processing stage) 41
5.2.2 訓練階段(Training stage) 41
5.2.3 測試階段(Testing stage) 46
第六章、編碼單元快速決策演算法性能比較 48
6.1 環境設置 48
6.2 效能分析 52
6.3 不同模型與演算法性能比較 57
6.4移動向量值的可視化比較 61
第七章、結論與未來展望 63
參考文獻 64

參考文獻

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指導教授

林銀議(Yin-Yi Lin)

審核日期

2021-8-11

推文