一種結合CNN與Random Forest 應用於H.266/VVC畫面內編碼之快速演算法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：25

、訪客IP：18.118.2.15

姓名

陳伯豪(Po-Hao Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

一種結合CNN與Random Forest 應用於H.266/VVC畫面內編碼之快速演算法
(Fast CU Partition for H.266/VVC Intra Prediction with CNN and Random Forest)

相關論文

★ 10Gb/s MM XFP光收發模組設計與實現	★ 資訊產品自動化測試之研究
★ 高電流密度鰭式氮化鎵高電子遷移率電晶體研究	★ 電子郵件及壓縮檔案解碼之研究
★ 渦輪碼在光學記錄系統上之應用	★ 離散餘弦轉換硬體架構之研究
★ 動態影像之錯誤隱藏研究	★ 即時性無失真壓縮編碼之研究
★ 類神經網路在手寫數字辨識之研究	★ 事後機率演算法則在資料儲存系統之研究
★ 紅外線傳輸協定及通道之研究	★ 低密度同位元檢查碼在數位資料儲存系統之研究
★ 一種新型的JPEG2000竄改偵測與還原技術	★ 即時性無失真壓縮之研究
★ 混合快速模式決策演算法之研究	★ 光學記錄MEPR2通道系統之時序恢復探討與研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-1-10以後開放)

摘要(中)

5G 通信世代帶來高速且低延遲的高品質傳輸技術，此外隨著電腦運算速度加快，視訊編碼標準從高效視訊編碼(HEVC/H.265)的 4K 畫質，提高到通用視訊編碼標準(Versatile Video Coding, VVC/H.266)的 8K 畫質。在 VVC 區塊編碼(coding unit, CU)架構下，除了在 HEVC/H.265 中的四分樹分割(Quad Tree, QT)模式，H.266/VVC 又增加二分樹分割(Binary Tree, BT)，以及三分樹(Ternary Tree, TT)，即多分樹劃分(QuadTree plus Multi-Type tree, QTMT)。VVC畫面內預測模式也從原先的 35 種增加到 67 種。這些新技術讓 H.266/VVC 編碼效能大幅提升，但也需要更高的運算複雜度，實驗顯示H.266 畫面內編碼所需時間為 H.265/HEVC 的 18 倍。本論文針對 H.266 畫面內編碼架構，提出了結合卷積神經網路(Convolutional Neural Networks，CNN)與隨機森林分類器(Random Forest Classifier)快速編碼模式決策演算法。不同於原始VVC遞迴運算比較所有的切割模式，本論文一開始先利用卷積神經網路預測四分樹(QT)的切割與否，再透過隨機森林分類器來預測多類型樹(MT)的切割模式，藉此減少H.266/VVC 編碼中畫面內編碼的複雜度。最終實驗結果顯示，與VVC相比，整體平均BDBR上升1.48%的情況下，編碼時間大約可以節省68.63%。

摘要(英)

The 5G communication generation brings high-speed and low-latency high-quality transmission technology. In addition, with the acceleration of computer computing speed, the video coding standard has been improved from the 4K quality of high-efficiency video coding (HEVC/H.265) to the universal video coding standard (Versatile Video Coding, VVC/H.266) 8K quality. Under the VVC block coding (coding unit, CU) architecture, in addition to the quad tree partition (Quad Tree, QT) mode in HEVC/H.265, H.266/VVC adds a binary tree partition (Binary Tree, BT ), and Ternary Tree (TT), that is, QuadTree plus Multi-Type tree (QTMT). The VVC intra-picture prediction modes have also increased from the original 35 to 67. These new technologies have greatly improved the encoding performance of H.266/VVC, but also require higher computational complexity. Experiments show that the time required for H.266 intra-screen encoding is 18 times that of H.265/HEVC. This paper proposes a fast coding mode decision algorithm combining Convolutional Neural Networks (CNN) and Random Forest Classifier (Random Forest Classifier) for the H.266 intra-picture coding architecture. Different from the original VVC recursive operation to compare all the cutting modes, this paper first uses the convolutional neural network to predict whether the quadrature tree (QT) is cut or not, and then uses the random forest classifier to predict the multi-type tree (MT) The cutting mode of H.266/VVC reduces the complexity of intra-picture coding in H.266/VVC coding. The final experimental results show that that the proposed fast encoding method can reduce up to 68.63% of encoding time with just 1.48% increase in BDBR as compared to the default VTM7.0.

關鍵字(中)

★ 通用視頻編碼
★ 畫面內預測
★ 編碼單元
★ 快速深度決策
★ 隨機森林
★ 卷積神經網路

關鍵字(英)

★ Versatile video coding
★ intra picture prediction
★ coding unit
★ fast depth decision
★ random forest
★ convolutional neural network

論文目次

章節目錄
論文摘要 I
Abstract II
誌謝 III
章節目錄 IV
附圖索引 VII
附表索引 X
第一章緒論 1
1.1研究動機及目的 2
1.2論文架構 3
1.3 H.266/VVC與 H.265/HEVC 差異 3
1.4 H.266/VVC 視訊編碼標準介紹 4
1.4.1編碼單元(Coding Unit) 5
1.4.2多類型樹(QTMT)的架構 6
1.4.3多類型樹(QTMT)結構的劃分機制 8
1.4.4編碼單元(CU)針對冗餘的劃分限制 9
1.4.5畫面內編碼預測(Intra Predict)介紹 11
1.4.6量化參數(Quantization Parameter) 13
1.4.7碼率失真代價函數(RD cost) 15
1.4.8 VVC架構(Configuration) 17
1.5機器學習( Machine Learning) 19
1.5.1支持向量機(Support Vector Machine) 19
1.5.2 隨機森林(Random Forest) 22
1.6 深度學習(Deep Learning) 27
1.6.1 類神經網路(Neural Network) 27
1.6.2 卷積神經網路(Convolutional Neural Network) 29
第二章相關文獻回顧 35
2.1 H.265/HEVC 之快速 CU 分割演算法相關文獻 35
2.1.1 Computational Complexity Reduction for HEVC Intra Prediction with SVM 35
2.1.2 Reducing Complexity of HEVC: A Deep Learning Approach 41
2.1.2 CNN-based CU Partition for HEVC Intra Prediction 47
2.2 H.266/VVC 快速 CU 分割演算法相關文獻 51
2.1.2 Speed Up H.266/QTMT Intra-Coding Based on Predictions of ResNet and Random Forest Classifier 51
2.1.2 DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC 57
第三章結合CNN與Random Forest 應用於快速編碼區塊決策演算法 64
3.1 快速編碼單元決策演算法 65
3.1.1 階段一卷積神經網路 66
3.1.2 階段二隨機森林分類器 67
3.2整體系統流程 68
3.2.1 劃分模式分析 68
3.2.2 特徵提取 69
3.2.3 訓練階段 73
3.2.4 測試階段 79
3.3 演算法性能比較 80
3.3.1 效能分析 81
3.3.2 不同演算法性能比較 82
第四章結合SVM、CNN與Random Forest 應用於快速編碼區塊決策演算法 85
4.1 加入支持向量機之快速編碼單元決策演算法流程 85
4.1.1前處理階段 87
4.1.2訓練階段 88
4.1.3效能分析 89
4.2 結合特徵之決策閾值 91
4.2.1編碼區塊分類之閥值設定 92
4.2.2加入標準差閾值之效能分析 94
第五章結論與未來展望 98
參考文獻 99

參考文獻

[1] J. An, H. Huang, K. Zhang. Quadtree plus binary tree structure integration with JEM tools, JVET-B0023, Joint Video Exploration Team (JVET). Feb. 2016.
[2] Shiba Kuanar,K.R. Rao,Christopher Conly, “Fast Mode Decision In Hevc Intra Prediction, Using Region Wise CNN Feature Classification”, 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).
[3] X. Shen and L. Yu, “CU splitting early termination based on weighted SVM,” EURASIP Journal on Image and Video Processing, vol. 1, pp. 1, 2013
[4] R. H. Gweon, Y.-L Lee, and J. Lim. Early termination of CU encoding to reduce HEVC complexity, JVTVC-F045, ITU-T/ISO/IEC Joint Collaborative Team on Video Coding (JCT-VC). Jul. 2011.
[5] F. Duanmu, Z. Ma, and Y. Wang, “Fast mode and partition decision using machine learning for intra-frame coding in HEVC screen content coding extension,” IEEE J. Emerg. Sel. Topics Circuits Syst., vol. 6, no. 4, pp. 517–531, Dec. 2016.
[6] Tao Zhang,Ming-Ting Sun,Debin Zhao,Wen Gao, “Fast Intra-Mode and CU Size Decision for HEVC”, IEEE Transactions on Circuits and Systems for Video Technology ( Volume: 27 , Issue: 8 , Aug. 2017 ).
[7] S.J Cai, “Reduction of computation complexity for HEVC intra prediction with support vector machine,” National Central University, Master Thesis, Jun 2017.
[8] Tianyi Li,Mai Xu,Xin Deng, “ A deep convolutional neural network approach for complexity reduction on intra-mode HEVC”, 2017 IEEE International Conference on Multimedia and Expo (ICME).
[9] Mai Xu,Tianyi Li,Zulin Wang,Xin Deng,Ren Yang,Zhenyu Guan, “Reducing Complexity of HEVC: A Deep Learning Approach”, IEEE Transactions on Image Processing ( Volume: 27 , Issue: 10 , Oct. 2018 ).
[10] Y. Li, Z. Liu, X. Ji and D. Wang, "CNN Based CU Partition Mode Decision Algorithm for HEVC Inter Coding", 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 993-997, 2018.
[11] H. Yang et al., “Low complexity CTU partition structure decision and fast intra mode decision for versatile video coding,” IEEE Transactions on Circuits and Systems for Video Technology, 2019.
[12] G. Tang, M. Jing, X. Zeng, and Y. Fan, “Adaptive CU split decision with pooling- variable CNN for VVC intra encoding,” IEEE Visual Communications and Image Processing (VCIP), pp. 1-4, 2019.
[13] Tzong-Dar Wu,Yuting Yen,J. H. Wang,R. J. Huang;Hung-Wei Lee and Hsuan-Fu Wang, "Automatic Target Recognition in SAR Images Based on a Combination of CNN and SVM",2020 International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM).
[14] D. B. Desai and S. N. Kavitha, "Face anti-spoofing technique using CNN and SVM", Proc. Int. Conf. Intell. Comput. Control Syst. (ICCS), pp. 37-41, May 2019.
[15] D. U. N. Qomariah, H. Tjandrasa and C. Fatichah, "Classification of Diabetic Retinopathy and Normal Retinal Images using CNN and SVM", 2019 12th International Conference on Information & Communication Technology and System (ICTS), pp. 152-157, 2019.
[16] Jie-Jay Wang, Yin yi Lin ,“Computation Reduction of HEVC Intra Prediction using combined SVM and CNN”, National Central University, Master Thesis, Jan 2020.
[17] Han-Yuan Hsu, Yin yi Lin, “Low Computational Complexity, High Coding Efficiency Intra Prediction for HEVC,” Master Thesis, National Central University, Jun. 2016.
[18] Hao-Chiun Wang, Yin yi Lin ,“CNN-based CU Partition for HEVC Intra Prediction”, National Central University, Master Thesis, July 2020.
[19] Xingang Liu, Yinbo Liu, “An Adaptive Mode Decision Algorithm Based on Video Texture Characteristics for HEVC Intra Prediction”, IEEE Transactions on Circuits and Systems for Video Technology Aug. 2017
[20] Yongfei Zhang,Gang Wang,Rui Tian,Mai Xu,C. C. Jay Kuo, “Texture-Classification Accelerated CNN Scheme for Fast Intra CU Partition in HEVC”, 2019 Data Compression Conference (DCC).
[21] F. Pedregosa et al., “Scikit-learn: Machine learning in python,” Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
[22] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
[23] Wenpeng Ren,Jia Su,Chang Sun,Zhiping Shi, “An IBP-CNN Based Fast Block Partition For Intra Prediction”, 2019 Picture Coding Symposium (PCS)
[24] Tianyi Li , Mai Xu , Runzhi Tang, Ying Chen, and Qunliang Xing “DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC”, 31 May 2021 ,IEEE.
[25] Qiuwen Zhang, Yihan Wang “Fast CU Partition and Intra Mode Decision Method for H.266/VVC” IEEE Access, 2020.

指導教授

林銀議(Yin-Yi Lin)

審核日期

2023-1-16

推文