HEVC畫面內/間加速預測編碼之相互影響探討

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姓名

齊繁鴻(Chi-Fan Hung) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

HEVC畫面內/間加速預測編碼之相互影響探討
(Discussion on the mutual influence of HEVC intra/inter accelerated prediction coding)

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至系統瀏覽論文 (2027-1-22以後開放)

摘要(中)

隨著科技進步影像的品質、解析度也隨之增加，資料量也越來越大，HEVC(High Efficiency Video Coding)又稱為H.265，採用更新的技術來降低位元率，連帶的也提升編碼計算複雜度。本論文採用深度學習與機器學習中的卷積神經網路CNN ( Convolutional Neural Network ) 和支持向量機SVM ( Support Vector Machine )，應用於HEVC編碼單元決策。將一個CTU分類成深度0、深度0~1、深度0~2、深度0~3四種類別，再利用卷積神經網路分層向下細分，藉由HEVC遞迴運算處理編碼單元的方式，在特定深度提前終止後續編碼計算。另外在畫面內使用支持向量機的決策閥值，透過特定條件減少進入卷積神經網路的次數以利於節省編碼時間。整體平均BDBR上升至4.72%，編碼時間平均可節省75.22%，並探討在Random Access和Low Delay架構下性能比較，以及不同GOP大小產生的影響。

摘要(英)

With the advancement of technology, the quality and resolution of images have also increased, and the amount of data becomes larger and larger. HEVC (High Efficiency Video Coding), also known as H.265, uses newer technology to reduce the bit rate, and the associated Improve coding computational complexity. This paper uses the convolutional neural network (CNN) and the support vector machine (SVM) in deep learning and machine learning that have flourished in recent years to apply it to HEVC coding unit decision-making. At the beginning of encoding, SVM is first used to classify the coding unit depth and prediction unit mode, and a CTU is classified into four categories: depth 0, depth 0~1, depth 0~2, and depth 0~3, and then the convolutional neural network is used. The network layer is subdivided downward. By using HEVC′s recursive operation to process coding units, subsequent coding calculations are terminated early at a specific depth. In addition, the decision threshold of the support vector machine is used to reduce the number of entries into the convolutional neural network through specific conditions to save coding time. The overall average BDBR increases to 4.72%, and the encoding time can be saved by 75.22% on average. Discuss the performance comparison between Random Access and Low Delay architectures, and the impact of different GOP size.

關鍵字(中)

★ 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
★ 深度決策

關鍵字(英)

★ HEVC
★ SVM
★ CNN
★ Random Access
★ Low Delay

論文目次

章節目錄
論文摘要 I
Abstract II
圖目錄 VIII
表目錄 XI
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介紹 3
2.1 視訊編碼H.265 / HEVC介紹 3
2.2 編碼架構H.265 / HEVC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) 9
第三章、支持向量機及深度學習介紹 18
3.1支持向量機(Support Vector Machine, SVM) 18
3.2深度學習介紹 20
3.2.1 類神經網路(Artificial Neural Network, ANN) 21
3.2.2 深度神經網路(Deep Neural Networks, DNN) 21
3.2.3 卷積神經網路(Convolutional Neural Networks, CNN) 22
第四章、相關文獻回顧 26
4.1 利用支持向量機減少編碼單元複雜度相關文獻回顧 26
4.1.1 Reduction of Computational Complexity HEVC Inter Prediction With Support Vector Machine 26
4.2利用CNN減少CU編碼複雜度相關文獻回顧 32
4.2.1 Fast CU Depth Decision for HEVC Using Neural Networks 33
4.2.2 SVM/CNN-based CTU partition for HEVC inter prediction 35
第五章、HEVC畫面內/間加速預測編碼效應探討 38
5.1 隨機存取(Random Access)情境下之探討 38
5.1.1 編碼性能(BDBR) 、PSNR比較 38
5.1.2 編碼時間比較(time saving) 44
5.1.3 實驗結果分析 48
5.2 低延遲 (Low Delay)情境下之探討 50
5.2.1 編碼性能(BDBR) 、PSNR比較 50
5.2.2 編碼時間比較(time saving) 56
5.2.3 實驗結果分析與討論 60
5.3 隨機存取與低延遲實驗結果討論 62
5.3.1環境設置 62
5.3.2隨機存取(Random Access)與低延遲(Low Delay)結果比較 63
第六章、結論與未來展望 67
參考文獻 68

參考文獻

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指導教授

林銀議(Yinyi Lin)

審核日期

2024-1-22

推文