結合支持向量機與摺積神經網路以提升HEVC編碼效能之研究

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姓名

張詠鈞(YUNG-CHUN CHANG) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

結合支持向量機與摺積神經網路以提升HEVC編碼效能之研究
(A Combined Support Vector Machine and Convolutional Neural Network Architecture for HEVC)

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摘要(中)

隨著科技的日新月異，人們對於高畫質的追求始終鍥而不捨，因此高解析度的顯示器及影像產品也就越來越多，而為了能夠有效壓縮高解析度中的龐大資料量，HEVC( High Efficiency Video Coding )使用許多方法來有效的降低位元率。然而為了更精進位元率以及畫質的表現，在畫面間預測上，我們應用支持向量機SVM ( Support vector machine )來對編碼單元深度以及預測單元模式做分類，編碼單元以畫面間預測的移動向量值的資訊、合併模式的CBF、鄰近區塊深度資訊做為特徵(Feature)將一個CTU分類成只做深度0、深度0~1、深度0~2、深度0~3四種類別，以此略過特定深度的運算。預測單元以畫面間預測的移動向量值的資訊、Skip flag、鄰近區塊RDO資訊做為特徵(Feature)，判斷預測單元做完Inter2N×2N後是否需要提前中止，進而節省掉後續預測模式所需花費的運算時間，不僅如此，我們再結合近年來日益普及的摺積神經網路CNN ( Convolutional Neural Network ) 於HEVC中的環路濾波器( In-Loop filter )來提高畫質的表現。由於藉由SVM分類的圖片中有相似的性質來訓練神經網路，比起未分類深度可以達到更好的提升效果。最後結合兩種演算法與CNN來與HEVC進行比較。而在畫面內預測上，擷取原始畫面的資訊以及空間上的相關性做為特徵，把CTU分為只做深度0~2以及深度0~3兩種組別，依照輸入特徵給予SVM預測結果來判斷是否當前CU要提早略過或提早終止，並也在其HEVC中的環路濾波結合以分類結果所訓練出的CNN模型來提升影像品質。此研究不僅在HEVC畫面內預測上做改良，畫面間預測也有相當表現，各別依照不同SVM架構與匹配神經網路來達到提高影像的效果，於畫面內預測上我們能達到BD-PSNR (0.36 dB)、BD-BR (-6.2%)；畫面間預測上能達到BD-PSNR (0.25 dB)、BD-BR (-6.2%)甚至能減少6%的編碼時間。

摘要(英)

With the rapid development of technology, People are always persistent in pursuing the high quality of video. Therefore, multimedia devices like monitors, players that have high resolution started rapidly increasing in numbers. In order to compress the significant increasing of data storage effectively, HEVC utilize multiple techniques to efficiently decrease bitrate. In inter-pridection, for the better effects, we proposed SVM-based fast inter CU ( Coding Units) depth decision algorithm and SVM-based fast inter PU mode decision algorithm to reduce the computational complexity. In SVM-based fast inter CU depth decision algorithm, we can skip certain depth by using SVM with features, including motion vector variance, CBF of merge mode, neighboring CU depth to classify a CTU into depth 0, depth 0~1, depth 0~2 and depth 0~3. In SVM-based fast inter PU mode decision algorithm, we use SVM with features, including motion vector variance, skip flag, the information of neighboring RDO to classify whether do early termination at 2N×2N. Besides it, we also combine CNN model with SVM in In-Loop filter of HEVC. CNN is a more and more popular technique wich can help us not only to recognize images or objects but enhance performance of portrait recently. So we can use the models to deal with the reconstruct images and thence enhance the quality of pictures. With the similar natures of blocks which SVM classes with, blocks in the same groups are trained together. Consequently, we get the models with different effects for distinct groups respectively and due to the relationship between the groups and the models, we can get the better performance than the results obtained by only using CNN without SVM. Finally, we combine two algorithms and CNN to compare with HEVC. Furthermore, in intra-prediction, by applying SVM with features consist of the CUs’ information and space relation, it can develop the criterion of early CU splitting and termination so that we can speed up intra-prediction by classifying a CTU into depth 0~2, depth 1~3. Again, we also use the classifications to train CNN model, and introduce it in deblocking filter on purpose to enhance the image performance. We improve effect on intra-prediction as well as inter-prediction, and both they can get eminent achievement. Our experiment results that the method surpasses mode (HM) with BD-PSNR (0.36 dB), BD-BR (-6.2%) on intra-prediction and BD-PSNR (0.25 dB), BD-BR (-6.2%) on inter-prediction which can even get 6% time saving compared to HM16.0.

關鍵字(中)

★ HEVC
★ 去區塊濾波器
★ 支持向量機
★ 畫面間預測
★ 畫面內預測
★ 移動向量
★ RDO
★ 摺積神經網路
★ 深度學習

關鍵字(英)

★ HEVC
★ Deblocking filter
★ SVM
★ Inter Prediction
★ Intra Prediction
★ Motion Vector
★ NeighboringRDO
★ Convolutional Neural Network(CNN)
★ Deep Learning

論文目次

論文摘要 V
Abstract VII
誌謝 IX
章節目錄 X
附圖索引 XIV
附表索引 XVIII
第一章緒論 1
1.1高效率視訊編碼(HEVC)標準介紹 2
1.2高效率視訊編碼架構介紹 3
1.2.1編碼單元(Coding Unit) 4
1.2.2預測單元(Prediction Unit) 5
1.2.3轉換單元(Transform Unit) 6
1.2.4碼率失真代價函數(RD cost) 6
1.2.5 HEVC架構(Configuration) 8
1.2.6 環路濾波器(In-Loop filter) 10
1.3研究動機及目的 11
1.4論文架構 12
第二章畫面間與畫面內預測模式及環路濾波器及支持向量機與摺積神經網路介紹 13
2.1 畫面間預測介紹(Inter Prediction) 13
2.1.1合併模式決策介紹(Merge Mode Decision) 13
2.1.2畫面間模式決策介紹(Inter Mode Decision) 16
2.2畫面內預測介紹(Intra Prediction) 21
2.3 去塊濾波器(Deblocking filter) 24
2.3.1 去塊濾波器的判定 (Deblocking filter determination) 25
2.3.2 去塊濾波器的過程 (Deblocking filter process) 26
2.3.3 去塊濾波器的技術總結 (Summary of deblocking filter) 27
2.4樣點自適應補償(Sample Adaptive Offest) 30
2.4.1 融合模式(Merge) 31
2.4.2 邊界補償(Edge Offset, EO) 32
2.4.3 帶狀補償(Band Offset, BO) 33
2.5支持向量機(Support Vector Machine) 34
2.6 支持向量機應用於HEVC畫面間編碼單元快速決策演算法 39
2.6.1 支持向量機編碼單元特徵選取介紹 41
1. 移動向量變異數(Motion Vector Variance) 41
2. Coded Block Flag (CBF) 45
3. 鄰近編碼單元深度資訊 (Neighboring CU) 46
2.6.2 應用SVM的畫面間深度快速決策演算法 48
1. 量化參數(QP) 48
2. 訓練樣本(Training) 52
3. 效能分析及討論 54
2.7 支持向量機應用於HEVC預測單元快速決策演算法 59
2.7.1 支持向量機預測單元特徵選取介紹 61
1. 移動向量變異數 61
2. Skip Flag 64
3. 鄰近區塊RDO資訊 65
2.7.2 應用SVM的畫面間預測模式快速決策演算法 67
1. 訓練樣本(Training) 67
2. 效能分析及討論 69
2.7.3 合併SVM於編碼單元深度及預測單元模式之演算法 72
2.8 支持向量機應用於HEVC畫面內編碼單元快速決策演算法 73
2.8.1 SVM應用於畫面內編碼單元(CU)特徵選取介紹 73
2.8.2 應用SVM的畫面內快速深度決策演算法 78
2.9 深度學習(Deep Learning) 91
2.9.1 機器學習(Machine Learning) 91
2.9.2 摺積神經網路(Convolutional Neural Network) 93
2.10 相關文獻(Related works) 99
2.10.1 Cnn-based in-loop filtering for coding efficiency improvement 99
2.10.2 A convolutional neural network approach for post-processing in HEVC intra coding 101
2.10.3 Multi-modal/multi-scale convolutional neural network based in-loop filter design for next generation video codec 102
2.10.4 Deep learning based HEVC in-loop filtering for decoder quality enhancement 104
第三章結合支持向量機與摺積神經網路於HEVC環路濾波器提高畫面內預測表現之研究 106
3.1 訓練環境 109
3.1.1 深度學習框架 109
3.1.2 軟體及硬體配置 109
3.2整體系統架構 110
3.2.1 前處理與訓練樣本(Pre-processing & training samples) 111
3.2.2 摺積網路架構與訓練(CNN model & Training) 113
3.3 摺積神經網路應用於環路濾波器之畫面內預測效能 117
3.4 結合摺積神經網路與畫面內編碼單元快速決策演算法提高HEVC編碼效率 126
3.4.1 結合SVM畫面內演算法與CNN訓練及測試 126
3.4.2 結合支持向量機畫面內編碼單元深度快速決策演算法與摺積神經網路之效能分析 128
第四章結合支持向量機與摺積神經網路於HEVC環路濾波器提高畫面間預測表現之研究 139
4.1摺積神經網路應用於環路濾波器之畫面間預測效能 141
4.2結合摺積神經網路與畫面間編碼單元快速決策演算法提高HEVC編碼效率 145
4.2.1結合SVM畫面間演算法與CNN訓練及測試 146
4.2.2 結合支持向量機畫面間編碼單元深度快速決策演算法與摺積神經網路之效能分析 147
第五章結合支持向量機與摺積神經網路總性能分析 160
第六章結論與未來展望 164
參考文獻 165

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指導教授

林銀議(YIN-YI LIN)

審核日期

2020-1-17

推文