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# Appendices
# LIST OF ACRONYMS
LIST OF ACRONYMS

AI All-Intra. 46, 47

ALF Adaptive Loop Filter. 29, 33

AME Affine Motion Estimation. 29

AMVP Advanced Motion Vector Prediction. 42

AV1 AOMedia Video 1. 28, 54

AVC Advanced Video Coding. 17, 27-29, 34-38, 136

B-CNN Branch Convolutional Neural Network. 53

BD Bjontegaard Delta. 48, 49, 132

BD-PSNR Bjontegaard Delta PSNR. 48, 49

BD-rate Bjontegaard Delta-Rate. 31, 48-50, 63, 65, 79, 91, 106

BDOF Bi-Directional Optical Flow. 29

BT Binary Tree. 36, 52, 53, 82, 83

CABAC Context Adaptive Binary Arithmetic Coding. 33, 44

CIIP Combined Intra-Inter Prediction. 42, 43

CNN Convolutional Neural Network. 12, 13, 19, 20, 22, 51-60, 63-67, 69, 70, 74-81, 84, 88-91, 93, 101

CTC Common Test Condition. 46, 49, 60, 63, 65, 66, 78, 84, 87, 106, 107

CTU Coding Tree Unit. 12, 20, 32, 34, 35, 38, 43, 51-54, 57-60, 63, 66, 68-71, 73, 76-80, 84, 85, 89, 91, 96-99, 132

CU Coding Unit. 19, 21, 22, 32-44, 51-55, 57-63, 68, 69, 71-73, 76, 80, 84, 85, 91, 94-98, 102, 105-108, 110, 111, 132, 133

dB decibel. 49

DCT Discrete Cosine Transform. 27, 33

DST Discrete Sine Transform. 33
**DT** Decision Tree. 52, 53, 68, 69, 72

**EVC** Essential Video Coding. 54

**FCN** Fully Convolutional Network. 74

**FHD** Full High Definition. 24, 38

**FLOPs** floating point operations. 91

**FPS** Frames per Second. 25

**GOP** Group Of Picture. 26, 44–47, 132

**HBT** Horizontal Binary Tree. 36, 59, 73, 84, 132

**HD** High Definition. 23, 24, 26, 27

**HEVC** High Efficiency Video Coding. 17, 18, 22, 23, 26–29, 34–39, 51, 52, 54–56, 76, 77, 93, 101, 136

**HM** HEVC Test Model. 26, 30, 31, 132

**HTT** Horizontal Ternary Tree. 37, 59, 73, 84, 132

**IEC** International Electrotechnical Commission. 23

**IPM** Intra Prediction Mode. 29, 39

**ISO** International Organization for Standardization. 23

**ISO/IEC** International Organization for Standardization / International Electrotechnical Commission. 27

**ISP** Intra SubPartition. 29, 40, 52

**ITU-T** International Telecommunication Union - Telecommunication Standardization Sector. 23, 27

**JEM** Joint Exploration Model. 52

**JVET** Joint Video Exploration Team. 23, 30, 52, 64, 65, 106

**KLT** Karhunen–Loève Transform. 33

**LD** Low-Delay. 46, 47

**LD** Low Definition. 24
**LDB** Low-Delay B. 46

**LDP** Low-Delay P. 46, 47, 132

**LFNST** Low Frequency Non-Separable Transform. 33

**LGBM** Light Gradient Boosting Machine. 53

**LMCS** Luma Mapping with Chroma Scaling. 33

**MAE** Mean Absolute Error. 63

**MB** MacroBlock. 34, 35

**MBMP-CNN** Multi-Branch Multi-Pooling CNN. 59, 67

**MIP** Matrix-based Intra Prediction. 40

**MLT-CNN** multi-level tree CNN. 53

**MPEG** Moving Picture Experts Group. 23

**MRL** Multi Reference Line. 29, 40

**MS-MVF** Multi-Scale Motion Vector Field. 76–78, 91

**MS-MVF-CNN** Multi-Scale Motion Vector Field CNN. 75, 82

**MSE** Mean Square Error. 48, 49

**MSE-CNN** Multi-Stage Exit CNN. 53

**MT** Multi-type Tree. 13, 20, 36, 52, 53, 58, 61, 62, 65, 67–74, 76, 79, 80, 82–84, 86, 88, 101

**MTS** Multiple Transform Set. 29, 33

**MTsplitMap** Multi-type Tree split Map. 72–74, 82, 84

**MV** Motion Vector. 40–42, 105

**MVD** Motion Vector Difference. 42

**NS** No Split. 38, 61, 62, 67, 73, 80, 82–84, 86, 97

**POC** Picture Order Count. 45, 46

**PSNR** Peak Signal-to-Noise Ratio. 48, 49, 98

**PU** Prediction Unit. 34–36, 38

**QHD** Quad High Definition. 24, 38
**QP** Quantization Parameter. 44, 49, 60, 63, 66, 77, 84, 87, 94–97, 108, 110

**QT** Quadtree. 13, 20, 29, 35, 36, 51–53, 56–60, 63, 68–73, 76, 80, 84, 85, 97, 101

**QTBT** quadtree plus binary tree. 52

**QTdepthMap** Quad Tree depth Map. 58, 60–63, 67, 68, 72–74, 80, 82, 84, 85

**QTMT** QuadTree with nested Multi-type Tree. 29, 37, 38, 52, 56, 58, 70, 71, 73, 91, 101, 132

**RA** RandomAccess. 22, 46, 47, 94, 101

**RAGOP32** RandomAccess Group Of Picture 32. 20, 60, 63, 65, 67, 77, 78, 84, 87, 91, 107

**RD** Rate Distortion. 49

**RD-cost** Rate-Distortion cost. 43, 44, 48, 71

**RDO** Rate-Distortion Optimization. 12, 22, 23, 26, 31, 32, 43, 44, 51, 52, 54, 57, 58, 60, 61, 63, 68, 71, 72, 79, 80, 95, 97, 98, 102, 105–108, 110, 111

**RF** Random Forest. 53, 56, 57, 65, 66, 68, 70, 84, 87, 88, 133, 136

**SAD** Sum of Absolute Differences. 44, 77

**SAO** Sample Adaptive Offset. 29, 33

**SATD** Sum of Absolute Transform Differences. 44, 52

**SD** Standard Definition. 24

**SVM** Support Vector Machine. 50, 51

**TL** Temporal Layer. 47

**TS** Time Saving. 63–65, 87, 88

**TT** Ternary-type Tree. 37, 53, 82, 83, 88

**TU** Transform Unit. 34–36, 38

**UHD** Ultra High Definition. 24, 26, 27, 38

**VBT** Vertical Binary Tree. 36, 59, 73, 84, 132

**VCA** Video Complexity Analyzer. 94

**VMAF** Video Multi-method Assessment Fusion. 48, 50, 98, 99
**VTM** VVC Test Model. 20, 26, 30, 31, 44, 46, 47, 50, 56–58, 60, 63–65, 67, 68, 70, 72, 73, 80, 84, 87, 88, 90, 91, 93, 105–108, 132, 133

**VTT** Vertical Ternary Tree. 37, 59, 73, 84, 132

**VVC** Versatile Video Coding. 11, 12, 15, 17–23, 26–34, 36–42, 51–58, 65, 67, 68, 70, 71, 77, 86, 87, 90, 91, 93, 94, 96, 98, 99, 101, 102, 105, 106, 136

**VVdeC** Versatile Video Decoder. 31

**VVenC** Versatile Video Encoder. 31, 93, 97–99, 102
# LIST OF FIGURES
LIST OF FIGURES

<table>
  <tbody>
    <tr>
      <td>1.1</td>
      <td>Chrominance sub-sampling [11]</td>
      <td>25</td>
    </tr>
    <tr>
      <td>1.2</td>
      <td>Resolution and frame rate of video</td>
      <td>25</td>
    </tr>
    <tr>
      <td>1.3</td>
      <td>History of international video coding standardization[12]</td>
      <td>27</td>
    </tr>
    <tr>
      <td>1.4</td>
      <td>Scope of Video Coding Standard[15]</td>
      <td>28</td>
    </tr>
    <tr>
      <td>1.5</td>
      <td>VTM versions vs HM. [25]</td>
      <td>30</td>
    </tr>
    <tr>
      <td>1.6</td>
      <td>Comparison of Runtime vs PSNR BD-rate.[26]</td>
      <td>32</td>
    </tr>
    <tr>
      <td>1.7</td>
      <td>Diagram of video coding system. [31]</td>
      <td>34</td>
    </tr>
    <tr>
      <td>1.8</td>
      <td>Example of CTU Partition in HEVC [32] (a) CTU partition (b) Corresponding partition tree structure</td>
      <td>35</td>
    </tr>
    <tr>
      <td>1.9</td>
      <td>Block Partitioning structure in AVC [32]</td>
      <td>36</td>
    </tr>
    <tr>
      <td>1.10</td>
      <td>Illustration of splitting CU to PUs in HEVC [32]</td>
      <td>37</td>
    </tr>
    <tr>
      <td>1.11</td>
      <td>Different split types in VVC</td>
      <td>37</td>
    </tr>
    <tr>
      <td>1.12</td>
      <td>Example of QTMT partition</td>
      <td>38</td>
    </tr>
    <tr>
      <td>1.13</td>
      <td>Number of split types per CU in VVC for Luma</td>
      <td>39</td>
    </tr>
    <tr>
      <td>1.14</td>
      <td>Intra prediction of CU</td>
      <td>39</td>
    </tr>
    <tr>
      <td>1.15</td>
      <td>Multiple Reference Line [31]</td>
      <td>40</td>
    </tr>
    <tr>
      <td>1.16</td>
      <td>Motion estimation [36]</td>
      <td>41</td>
    </tr>
    <tr>
      <td>1.17</td>
      <td>Affine motion model [31]</td>
      <td>42</td>
    </tr>
    <tr>
      <td>1.18</td>
      <td>Geometric Partitioning Mode [39]</td>
      <td>43</td>
    </tr>
    <tr>
      <td>1.19</td>
      <td>Example of GOP structure [41]</td>
      <td>46</td>
    </tr>
    <tr>
      <td>1.20</td>
      <td>LDP configuration of VTM</td>
      <td>47</td>
    </tr>
    <tr>
      <td>1.21</td>
      <td>Random Access of GOP size 16</td>
      <td>48</td>
    </tr>
    <tr>
      <td>1.22</td>
      <td>RD curves for BD metric</td>
      <td>49</td>
    </tr>
    <tr>
      <td>3.1</td>
      <td>Example of QTMT partitioning structure, partition and QTdepthMap for a 64×64 CTU</td>
      <td>57</td>
    </tr>
    <tr>
      <td>3.2</td>
      <td>Example of partition path with MT referring to VBT, HBT, HTT and VTT types.</td>
      <td>59</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>3.3</td>
      <td>Structure of proposed MBMP-CNN. The vector of three elements on top of Resblocks and Conv2D layers represents kernel size, number of filters and stride respectively. Value "k" denotes pooling size for Maxpooling layers.</td>
      <td>60</td>
    </tr>
    <tr>
      <td>3.4</td>
      <td>Example of acceleration by QTdepthMap</td>
      <td>61</td>
    </tr>
    <tr>
      <td>3.5</td>
      <td>Flow chart of proposed acceleration method</td>
      <td>62</td>
    </tr>
    <tr>
      <td>3.6</td>
      <td>Complexity gains versus BD rate loss in comparison with RF based methods.</td>
      <td>65</td>
    </tr>
    <tr>
      <td>3.7</td>
      <td>Complexity gains versus BD rate loss for CNN models in ablation study.</td>
      <td>66</td>
    </tr>
    <tr>
      <td>4.1</td>
      <td>Possible partition paths for a final partition given by split boundaries</td>
      <td>72</td>
    </tr>
    <tr>
      <td>4.2</td>
      <td>Example of QTMT partition, tree representation, QTdepthMap and MT-splitMaps of CTU size of 64×64.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>4.3</td>
      <td>Multi-Scale Motion Vector Field CNN. The vector above Resblock and Conv2D represents (kernel size, number of filters, stride).</td>
      <td>74</td>
    </tr>
    <tr>
      <td>4.4</td>
      <td>U-Net feature extractor and MT branch module</td>
      <td>75</td>
    </tr>
    <tr>
      <td>4.5</td>
      <td>Comparison of performances between PIX-CNN and MVF-CNN.</td>
      <td>78</td>
    </tr>
    <tr>
      <td>4.6</td>
      <td>Flowchart of acceleration algorithm</td>
      <td>81</td>
    </tr>
    <tr>
      <td>4.7</td>
      <td>Distribution of split types for MT0, MT1, MT2</td>
      <td>83</td>
    </tr>
    <tr>
      <td>4.8</td>
      <td>Curves of accuracy and Thm for MT0, MT1, MT2.</td>
      <td>86</td>
    </tr>
    <tr>
      <td>4.9</td>
      <td>Comparison of performances between proposed method and reproduction of [66].</td>
      <td>90</td>
    </tr>
    <tr>
      <td>5.1</td>
      <td>Average percentage of the CUs with CU height and width larger, equal to, or smaller than the reference encoding.</td>
      <td>94</td>
    </tr>
    <tr>
      <td>5.2</td>
      <td>Diagram of CU size based fast partitioning.</td>
      <td>95</td>
    </tr>
    <tr>
      <td>5.3</td>
      <td>Fast approach involved partition search.</td>
      <td>96</td>
    </tr>
    <tr>
      <td>5.4</td>
      <td>The comparison between the standalone VVenC and the proposed fast multi-rate encoding.</td>
      <td>97</td>
    </tr>
    <tr>
      <td>1</td>
      <td>High-level view of the RDO process involving partitioning, test modes and possible VTM shortcuts.</td>
      <td>105</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Encoding time in different QPs comparing to QP 22.</td>
      <td>106</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Complexity distribution for block sizes in QP 22 and QP 37</td>
      <td>107</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Selection rate for different block sizes</td>
      <td>108</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Pie chart of complexities of inter coding modes.</td>
      <td>109</td>
    </tr>
  </tbody>
</table>
<table><tr><td>6</td><td>Selection rate of inter coding modes</td><td>109</td></tr><tr><td>7</td><td>Stacked chart of selected inter modes in different block sizes</td><td>110</td></tr></table>
# LIST OF TABLES
LIST OF TABLES

<table><tr><td>1.1</td><td>Comparison between HEVC and AVC</td><td>29</td></tr><tr><td>1.2</td><td>Comparison between VVC and HEVC</td><td>29</td></tr><tr><td>1.3</td><td>Data types to transmit for motion data coding</td><td>42</td></tr><tr><td>3.1</td><td>Performance of the proposed method in comparison with reference CNN-based methods</td><td>64</td></tr><tr><td>4.1</td><td>Settings of split type in VTM9 under RA [33]</td><td>82</td></tr><tr><td>4.2</td><td>Table of confusion for SkipMt (Unit: %)</td><td>85</td></tr><tr><td>4.3</td><td>Breakdown of sequences used to train RFs of [66]</td><td>88</td></tr><tr><td>4.4</td><td>Performance of the proposed method in comparison with reference CNN-based methods</td><td>89</td></tr><tr><td>4.5</td><td>Overhead of our method (Unit: %)</td><td>90</td></tr></table>
<figure><img src="image_56.png" /></figure>
**Titre :** Apprentissage pour l'encodage vidéo nouvelle génération

**Mot clés :** VVC, Codage inter, Réduction de la complexité, CNN, Partitionnement

**Résumé :** L'encodage vidéo avec le dernier codec Versatile Video Coding (VVC) requiert d'importantes ressources de calcul. Malgré son impact sur le temps d'encodage global, peu d'études portent sur l'accélération de l'encodage inter. Cette thèse se concentre ainsi sur ce sujet, en proposant des approches de partitionnement rapide.

Notre première contribution consiste à utiliser un CNN léger pour réduire l'espace de recherche de partitionnement. En estimant la carte de profondeur des décisions de partitionnement QT, ce CNN nous permet d'élaguer l'espace de recherche de l'arbre MT initial. Les expériences montrent que ce travail peut réduire d'un tiers la complexité, pour une perte légère en efficacité de codage.

Dans la deuxième partie, nous améliorons la première contribution en proposant une nouvelle structure CNN associé à un algorithme d'accélération. La carte de profondeur QT et les décisions MT sont prédites simultanément, réduisant davantage l'espace de recherche. Le compromis efficacité et accélération d'encodage obtenu surpasse l'état de l'art.

Dans la dernière partie, nous proposons une méthode de partitionnement rapide multi débit pour les scénarios de streaming. Les données collectées à partir d'encodages à bas débits sont exploités pour accélérer les encodages à débits supérieurs. 40% de la complexité est réduite en appliquant notre approach à l'encodeur VVenc, avec une perte raisonnable.

**Title:** Learning for new generation video coders

**Keywords:** VVC, Inter coding, Complexity reduction, CNN, Partitioning

**Abstract:**

Video encoding with the latest video codec Versatile Video Coding (VVC) requires significant computational resources. Despite its impact on the overall encoding time, few studies focus on accelerating inter-frame encoding. This thesis, therefore, targets this topic by proposing fast partitioning approaches.

Our first contribution involves utilizing a lightweight CNN to reduce the search space of partitioning. By predicting the QT depth map, this CNN allows us to prune the search space of the MT partitions. Experiments show that this work can reduce complexity by a third, with slight coding loss.

In the second part, we intend to improve the first contribution by proposing a novel CNN structure associated with an acceleration algorithm. The QT depth map and the MT decisions are predicted simultaneously, further reducing the search space. The trade-off between efficiency and encoding acceleration of our method outperforms the state of the art.

In the final part, we propose a multi-rate fast partitioning process for streaming scenarios. Partition data collected from low-bitrate encodings are leveraged to accelerate high-bitrate encodings. 40% reduction in complexity is achieved by applying our approach to the VVenc encoder, with a reasonable loss.