臺灣博碩士論文加值系統

多媒體通信傳播品質藉由高效率視訊編碼(HEVC)技術顯著提升效能。為達到優良的編碼效率，HEVC使用了許多新的編碼技術，如:編碼單位(Coding Unit(CU))、預測單位(Predict Unit(PU))和轉換單位(Transform Unit(TU))，因此增大了運算複雜度。HEVC需要大量運算時間對所有的CU與PU區塊分割模式估算編碼效能以決定最好的區塊分割方式。針對此一高運算複雜度的問題，本論文提出快速CU與PU編碼模式決策的方法，在不降低品質的前提下減少HEVC編碼運算複雜度。在我們的方法中，基於CU切割流程中率-失真衡量數據(RD-Cost)、鄰居區塊的深度資訊，以及當前區塊資訊，提出了兩種提早終止(early termination)CTU最佳結構決策流程的方法。其中方法一基於「RD-Costs」、「鄰近區塊深度資訊」，並使用類神經網路將所得前述所得之資訊作為輸入特徵預測當前深度CU是否適合提前終止；方法二基於「當前區塊PU 2N×2N之資訊」與「前一層深度之切割情況」作為類神經網路之輸入，利用此類神經網路來決策當前PU是否需要測試更小之深度，最後將此兩種方法結合成一快速HEVC決策方法。實驗結果顯示，我們所提出的演算法，整體而言在BDBR僅上升1.87%的情況下，編碼時間上可以達到64.41%的加速效果，達到大幅度降低HEVC運算複雜度之目的。

The high efficienct video coding standard, HEVC, was proposed to enable high quality multimedia commnications. To achieve high coding efficiency, it has to determine the best coding unit (CU), prediction unit (PU), and transform unit (TU) through exhaustive search. It is time consuming and requires to speedup the CU and PU mode decisione process without degrading the coding quality. In this thesis, we proposed to speed up the HEVC inter-frame CU and PU mode decision process, in which rate-distortion cost, coding depth level of neighboring blocks, and current block information, are adopted as the input parameters of machine learning function to predict whether current depth CU an PU need to split or not. To fast decide the coding mode, it can: (1) utilize depth information and rate-distortion costs of both the neighboring CU and the current PU as the input parameters of neural network to predict whether the current depth CU need to split or not; (2) utilize RD-cost of the current block PU 2N2N and MSM mode, the upper depth information, such as whether the upper depth PU split or not, and the upper depth best mode RD-costs, as the input of neural network to determine whether the current depth PU mode needs further partition or not. In this research, both fast mode decision strategies are combined to yield a fast HEVC mode decision method. Experiments showed that the proposed method can reduced the encoding time on the average with 64.41%, and increases BD-bitrate about only 1.87%, as compared to the standard HEVC codec, HM13.0.

摘要 1
Abstract 2
致謝 3
目錄 4
圖目錄 6
表目錄 8
第一章 緒論 9
1.1 研究動機與目的 9
1.2 問題描述與研究方法 9
1.3 論文組織 11
第二章 背景知識 12
2.1 HEVC視訊編碼標準介紹 12
2.1.1 HEVC制定 12
2.1.2 HEVC網路提取層(NAL) 14
2.1.3 HEVC視訊編碼層(VCL) 14
2.1.3.1 編碼單位(Coding, CU) 15
2.1.3.2 預測單位(Prediction Unit, PU) 17
2.1.3.3 轉換單位(Transform Unit, TU) 26
2.1.3.4 率失真最佳化(Rate-Distortion Optimization Routine) 27
2.1.3.5 轉換與量化(Transform and Quantization) 30
2.1.3.6 熵編碼(Entropy Encoding) 31
2.2 機器學習之相關背景知識 34
2.3.1機器學習運作流程 35
第三章 HEVC快速編碼單位決策方法 38
3.1 相關文獻探討 38
3.2 運用類神經網路之HEVC快速決策法 43
3.2.1 類神經網路輸入特徵分析 43
3.2.2 類神經網路之應用方法 54
3.2.2.1 倒傳遞類神經網路 56
3.2.2.2快速CU決策之類神經網路架構 58
3.2.2.3 快速PU決策之類神經網路架構 60
第四章 實驗結果與討論 62
4.1 實驗環境設置 62
4.2 方法一與文獻[16]之實驗結果比較 64
4.3 方法二與文獻[18]及文獻[19]之實驗結果比較 67
4.4 結合本文所提出的方法一與方法二之實驗結果 72
第五章 結論與未來研究探討 81
5.1 結論 81
5.2 未來研究探討 82
參考文獻 83

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