分散式編碼用於VVC/H.266

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鍾承學(Cheng-Hsueh Chung) 查詢紙本館藏

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通訊工程學系

論文名稱

分散式編碼用於VVC/H.266
(Distributed Video Coding On Versatile Video Coding)

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至系統瀏覽論文 (2026-1-10以後開放)

摘要(中)

在這日新月異的時代，隨著網路的進步以及科技的發達，人們對於追求更高品質的事物始終不會停滯，對於高解析度的影像也是如此，為了能夠更有效率的壓縮這些巨大的視訊資料量，VVC採用了一些更新穎的技術，如矩形編碼樹單元、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，即卷積神經網路與隨機森林分類器，將其應用於VVC編碼單元編碼區外的劃分。不同於原始VVC遞迴運算編碼單元碼率失真成本，本論文在編碼一開始時先使用支持向量機及卷積神經網路，將方形編碼單元區塊做出劃分，再利用隨機森林分類器向下細分矩形編碼單元區塊，分類完成的區塊將只會進行一次的編碼，藉此大幅節省編碼所需時間，後續再透過隨機森林決策輔助原始VVC篩選預測模式的方式，將整體計算縮減至不到兩成。後續在解碼端則引入三通道殘差神經網路架構，以不同的資訊去補償我們在編碼端的失真。以此實現分散式視訊編碼的概念，結合快速預測模式與解碼端之後處理補償影像品質。實驗結果與VVC相比，整體平均BDBR下降1.63%的情況下，整體編解碼時間大約可以節省51.48%。

摘要(英)

In this ever-changing era, with the advancement of the Internet and the development of technology, people will never stop pursuing higher-quality things, and the same is true for high-resolution images. In order to compress these huge videos more efficiently data volume, VVC adopts some more novel technologies, such as rectangular coding tree unit, rate-distortion optimization, etc., but at the same time, it also causes an increase in the complexity of coding calculations. This paper combines the very popular in recent years Deep learning and machine learning, namely convolutional neural networks and random forest classifiers, are applied to VVC coding unit depth decisions. Different from the original VVC recursive operation coding unit rate distortion cost, this paper first uses support vector machine and convolutional neural network to divide the square coding unit blocks at the beginning of coding, and then uses random forest classifier to Subdividing the rectangular coding unit block, the classified block will only be coded once, thereby greatly saving the time required for coding, and then using random forest decision-making to assist the original VVC to filter the prediction mode, reducing the overall calculation to Less than 20%. Subsequently, a three-channel residual neural network architecture is introduced at the decoding end to compensate our distortion at the encoding end with different information. In this way, the concept of distributed video coding is realized, and the fast prediction mode is combined with post-processing at the decoding end to compensate for image quality. Experimental results Compared with VVC, when the overall average BDBR is reduced by 1.63%, the overall side decoding time can be saved by about 51.48%.

關鍵字(中)

★ 多功能影像編碼
★ 支持向量機
★ 卷積神經網路
★ 編碼單元
★ 分散式視訊編碼
★ 畫面內預測

關鍵字(英)

★ Versatile Video Coding
★ support vector machines
★ convolutional neural networks
★ coding units
★ distributed video coding
★ intra prediction

論文目次

論文摘要 VIII
Abstract IX
致謝 XI
圖目錄 XV
表目錄 XVIII
第一章、緒論 1
1.1研究動機與目的 1
1.2論文架構 1
1.3多功能影像編碼(Versatile Video Coding)簡介 2
1.4 VVC編碼架構介紹 3
1.4.1碼率失真代價函數 5
1.4.2編碼單元(Coding Unit) 6
1.4.3預測單元(Prediction Unit)及幀內預測(Intra Predict)介紹 8
1.4.4轉換單元(Transform Unit) 13
1.4.5量化參數(Quantization Parameter) 13
1.4.6 H.265/HEVC 和 H.266/VVC 差異 15
1.5支持向量機(Support Vector Machine)介紹 16
1.6深度學習介紹 19
1.6.1類神經網路 20
1.6.2深度學習 20
第二章、相關文獻回顧 25
2.1HEVC、VVC編碼端減少CU編碼複雜度相關文獻回顧 25
2.2利用支持向量機減少編碼單元複雜度相關文獻回顧 25
2.2.1 Computational Complexity Reduction for HEVC Intra Prediction with SVM 26
2.3利用CNN減少CU編碼複雜度相關文獻回顧 34
2.3.1 A deep convolutional neural network approach for complexity reduction on intra-mode HEVC 34
2.4VVC利用CNN及RFC減少CU編碼複雜度相關文獻 41
2.5分散式編碼相關文獻 43
第三章、VVC分散式壓縮編碼之探討 45
3.1兩階段編碼單元(CU)快速切割演算法則 45
3.1.1VVC新增劃分模式對於整體效能分析 45
3.1.2第一階段編碼單元劃分演算法則 49
3.1.2-1卷積神經網路架構訓練 52
3.1.2-2 前處理階段 52
3.1.2-3 訓練階段 53
3.1.2-4 效能分析 58
3.1.3第二階段編碼單元劃分演算法則 60
3.2隨機森林決策(RFD)輔助約略模式決策(RMD)演算法 67
3.2.1約略模式決策(RMD)畫面內預測模式 67
3.2.2約略模式決策(RMD)及隨機森林決策(RFD)各項分析 69
3.2.2-1約略模式決策(RMD)及隨機森林決策(RFD)對應全模式搜索準確率 69
3.2.2-2約略模式決策(RMD)及隨機森林決策(RFD)篩選模式之紋理比較 71
3.2.3隨機森林決策輔助約略模式決策以減少候選模式之演算法流程及合併編碼單元兩階段快速演算法則之效能分析 72
第四章、VVC分散式壓縮解碼及編解碼端綜合效能 76
4.1 各類卷積神經網路以後處理方式應用於VVC之解碼端比較 76
4.1.1 與編碼端相同卷積神經網路架構用於解碼端 78
4.1.2 三通道殘差網路架構用於解碼端 82
4.1.3 不同卷積神經網路用於解碼端效能分析 87
4.2 合併編解碼端以實現分散式編碼之各項效能分析 89
4.2.1 合併編解碼端之影像品質分析 89
4.2.2合併編解碼端時間分析 92
第五章、結論與未來展望 97
參考文獻 99

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指導教授

林銀議(Yin-Yi Lin)

審核日期

2023-1-16

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