漸進式特徵融合模型應用於 VVC 畫面間編碼之快速演算法

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：17

、訪客IP：3.144.87.133

姓名

謝尚融(HSIEH SHANG JUNG) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

漸進式特徵融合模型應用於 VVC 畫面間編碼之快速演算法
(Fast QTMT Partition Algorithm for VVC Inter Prediction with Hierarchical Feature Fusion Model)

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 (2027-12-5以後開放)

摘要(中)

現今科技日新月異,科技與人們的生活息息相關,但隨著數位內容需求的增加,尤其是在高解析度(如4K、8K甚至更高)的視頻領域,人們對於影像品質和視覺體驗的期望也越來越高,因此H.266/VVC採用許多先進的技術來解決人類的需求,隨之而來的影響是編碼計算複雜度的提升。
本論文運用機器學習跟深度學習的方式,將其應用在H.266/VVC畫面間預測,首先運用機器學習中的支持向量機(SVM)將CU做第一次劃分,接著運用卷積神經網路(CNN)將CU做第二次劃分,針對H.266/VVC新增的多類型樹劃分模式,採用隨機森林分類器(RFC)將CU劃分的更精細,最後再結合CU-PU Decision進行第三次的劃分,透過漸進式的劃分使得編碼過程,不需再計算冗長的碼率失真代價函數。第四章則探討將隨機森林分類器,銜接在不同位置上,所造成的影像品質與時間節省的差異。最終實驗結果與H.266/VVC相比,平均整體 BDBR 為2.50百分比,而編解碼時間可以達到64.67百分比的節省。

摘要(英)

In today’s rapidly evolving technological era, people′s lives are closely connected to technology. With the increasing demand for digital content, especially in high-resolution video(such as 4K, 8K,and even higher),expectations for image quality and visual experience are rising. Therefore, H.266/VVC employs many effective techniques to address these needs.
However,this comes with the consequence of increased encoding complexity.This paper applies machine learning and deep learning techniques to inter-frame prediction in H.266/VVC. First, the Support Vector Machine (SVM) from machine learning is used for the initial division of Coding Units (CUs). Next, a Convolutional Neural Network (CNN) is
applied for a second division of the CUs. For the new multi-type tree partitioning mode introduced by VVC, a Random Forest Classifier (RFC) is used to further refine the CU division.
Finally, the CU-PU Decision is combined for a third division. Through this progressive division, the encoding process avoids the need for calculating the lengthy rate-distortion cost
function. Chapter four explores the impact on image quality and time savings when connecting the Random Forest Classifier at different stages. The final experimental results show that,
compared to H.266/VVC, the average Bjontegaard Delta Bit Rate (BDBR) is 2.50%, and encoding/decoding time is 64.67%.

關鍵字(中)

★ 多功能影像編碼
★ 支持向量機
★ 卷積神經網路
★ 隨機森林分類器
★ 畫面間編碼預測
★ 深度學習

關鍵字(英)

★ Versatile Video Coding
★ Support Vector Machine
★ Convolutional Neural Network
★ Random Forest Classifier
★ Inter-frame Prediction
★ Deep Learning

論文目次

論文摘要 i
Abstract ii
致謝 iii
章節目錄 iv
圖目錄 vi
表目錄 ix
第一章序論 1
1.1研究動機及目的 1
1.2論文架構 1
1.3多功能影像編碼(VVC)簡介 2
1.4多功能影像編碼(VVC)架構介紹 3
1.4.1 編碼單元(Coding Unit, CU) 4
1.4.2 量化參數(Quantization Parameter, QP) 5
1.4.3 碼率失真代價函數(RD cost) 6
1.5畫面間預測模式(Inter Prediction)介紹 8
1.5.1 畫面間模式決策介紹(Inter Mode Decision) 9
1.5.2 進階移動向量預測(Adaptive Motion Vector Prediction, AMVP) 10
1.5.3 單向預測(Uni-Prediction) 10
1.5.4 雙向預測(Bi-Prediction) 11
1.5.5 擴展的合併預測模式(Extended Merge Prediction) 12
1.6支持向量機與卷積神經網路介紹 15
1.6.1 支持向量機(Support Vector Machine, SVM) 15
1.6.2 卷積神經網路(Convolution Neural Network, CNN) 17
第二章相關文獻回顧 22
2.1SVM-CNN應用於HEVC畫面間編碼單元切割之回顧 22
2.1.1 回顧SVM-CNN演算法 22
2.1.2 模型特徵選取 23
2.1.3 訓練樣本(Training) 27
2.2CU Skip -PU 2N快速決策演算法之回顧 32
2.2.1快速編碼單元(CU)決策算法 33
2.2.2 CU Skip – PU 2N快速決策演算法 36
2.3 HEVC畫面間編碼中CU及PU快速分割演算法之回顧 39
2.3.1 改善SVM-CNN之演算法 39
2.3.2 改善CU Skip - PU 2N之演算法 43
2.4 快速VVC畫面間預測編碼研究之回顧 45
2.4.1 SVM-CNN 應用於 VVC 快速演算法之統計分析與實驗結果 45
2.4.2 CU Skip- PU 2N 應用於 VVC 快速演算法之統計分析與實驗結果 48
2.4.3 結合 SVM-CNN/ CU Skip - PU 2N 應用於 VVC快速演算法之統計分析與實驗結果 52
第三章結合SVM-CNN/CU-PU切割法則應用於VVC畫面間編碼決策演算法 55
3.1SVM-CNN應用於VVC快速演算法 55
3.1.1 SVM應用於VVC編碼單元演算法之回顧及改進 55
3.1.2 結合SVM-CNN應用於VVC快速演算法之實驗結果與分析 62
3.2結合SVM-CNN/ CU-PU切割法則應用於VVC快速演算法 68
3.2.1 CU - PU切割法則應用於VVC快速演算法之回顧及改進 69
3.2.2 結合SVM-CNN/ CU-PU應用於VVC快速演算法之實驗結果與分析 73
3.2.3 演算法則在HEVC 與VVC性能比較 77
第四章結合隨機森林分類器與SVM-CNN/ CU-PU演算法則應用於快速編碼區塊決策演算法 81
4.1應用隨機森林分類器於多類型樹切割單元之演算法則 81
4.1.1 隨機森林分類器介紹 81
4.1.2 劃分模式分析與特徵提取 84
4.1.3 實驗結果與分析 89
4.2結合隨機森林分類器與SVM-CNN/ CU-PU演算法則應用於快速編碼區塊決策演算法則之性能分析 92
4.2.1 結合SVM-CNN-RFC/CU-PU之實驗結果與分析 92
4.2.2 結合SVM-CNN/CU-RFC-PU之實驗結果與分析 96
第五章結論與未來展望 101
參考文獻 102

參考文獻

[1] D. H. Yang, “SVM/CNN-based CTU partition for HEVC inter prediction,” Jan 2021, department of Communication Engineering, National Central University,Taiwan 32054, R.O.C.
[2] J.K. Liu, “Efficient HEVC inter prediction using SVM,” Department of Communication Engineering National Central University, Taiwan 32054, R.O.C.,Jan 2019.
[3] K. Kim and W. W. Ro, “Fast cu depth decision for HEVC using neural networks,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 5,pp. 1462–1473, 2019.
[4] Y. D. Tsai, “Research on fast HEVC inter prediction coding,” Jan 2019, department of Communication Engineering, National Central University, Taiwan 32054,R.O.C.
[5] T. Y. Wei, “Efficient cu and pu partitions for HEVC inter prediction,” Jan 2023,department of Communication Engineering, National Central University, Taiwan 32054, R.O.C.
[6] Y. P. Wang, “Research on Fast VVC Inter Prediction Coding,” Jan 2024,department of Communication Engineering, National Central University, Taiwan 32054, R.O.C.
[7] P. M. Chang, “CNN-Based Post-Processing for Performance Improvement in Feature Coding for Machines,” Dec 2024, department of Communication Engineering, National Central University, Taiwan 32054, R.O.C.
[8] C. Po Hao, “Fast cu partition for h.266/VVC intra prediction with CNN and random forest,” Jan 2023, department of Communication Engineering, National Central University, Taiwan 32054, R.O.C.
[9] J. Li, S. Zhang, and F. Yang, “Random forest accelerated cu partition for inter prediction in h.266/VVC,” in 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, pp. 01–06.
[10] J.-L. Lin, Y.-W. Chen, Y.-W. Huang, and S.-M. Lei, “Motion vector coding in the HEVC standard,” IEEE Journal of Selected Topics in Signal Processing, vol. 7, no.6, pp. 957–968, 2013.
[11] Y.-S. Ciou, M.-J. Chen, C.-H. Yeh, C.-R. Lu, M.-C. Hsieh, and C. Lo, “Fast multi-type tree partition for h.266/VVC inter coding,” in 2022 IEEE International Conference on Consumer Electronics Taiwan, 2022, pp. 471–472.
[12] C.-R. Lu, M.-J. Chen, and C.-H. Yeh, “Fast inter coding algorithm using conditional probability and correlation analysis for h.266/VVC,” in 2023 International Conference on Consumer Electronics Taiwan (ICCE-Taiwan), 2023,pp. 741–742.
[13] Tian yi Li, Mai Xu, Runzh 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.
[14] 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.
[15] 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.
[16] Z. Pan, P. Zhang, B. Peng, N. Ling, and J. Lei, “A CNN-based fast inter coding method for vvc,” IEEE Signal Processing Letters, vol. 28, pp. 1260–1264, 2021.
[17] F. Pedregosa et al., “Scikit-learn: Machine learning in python,” Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
[18] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
[19] 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

2024-12-12

推文