多功能視訊編碼(H.266/VVC)為最新的視訊編碼國際標準，能夠對超高畫質視訊進行高效率壓縮。相較於前一代的高效率視訊編碼標準(HEVC/H.265)四元樹切割，H.266/VVC以四元樹切割加上由二元樹與三元樹切割組成的巢狀多類型樹為編碼區塊結構，提供多樣的編碼單位切割，也新增許多先進的編碼工具，適用於各種視訊內容，進一步提高壓縮率，但也使編碼的計算量大幅增加，因此，加速編碼是非常重要的議題。本論文提出一個基於影像邊緣特徵之H.266/VVC畫面間快速編碼演算法，利用邊緣偵測所提取之影像特徵來決策編碼樹單位中編碼工具之使用，再利用編碼單位內的影像邊緣特徵，快速決策多類型樹中三元樹的切割與方向，並根據參考畫面的資訊來預測當前編碼樹單位的最大切割深度以提前終止編碼。本論文所提演算法之實驗結果顯示在random-access的架構下，平均可節省33.88%的編碼時間，且BDBR只有增加0.26%，效果優於參考文獻。

Versatile video coding (H.266/VVC) is the newest international video coding standard to effectively encode the ultra-high-definition video. Compared to the quadtree (QT) split of high efficiency video coding (HEVC/H.265), the quadtree with nested multi-type tree (QT-MTT) using binary tree (BT) and ternary tree (TT) splits coding block structure in H.266/VVC provides various sizes of coding unit (CU) partitioning. Furthermore, advanced coding tools are numerously equipped in the H.266/VVC encoder. The functionalities enhance the compression performance for a variety of video content but also greatly increase encoding complexity. Therefore, the acceleration of encoding is a very important issue. This thesis proposes a fast algorithm based on image edge feature for H.266/VVC inter coding. The image feature extracted by edge detection is utilized to decide the usage of the coding tools in the coding tree unit (CTU). The image feature is also applied for the fast decision of TT split and direction in the MTT structure of CU. Moreover, the maximum split depth of the CTU is predicted according to the reference frames for the early termination of encoding process. The experimental results show that the coding time is reduced by 33.88% while the Bjøntegaard delta bitrate (BDBR) is only increased by 0.26% on average under random-access configuration. The performance of proposed algorithm outperforms the previous work.

第一章 緒論 13
第二章 H.266/VVC快速演算法文獻回顧 43
第三章 所提出的畫面間編碼快速演算法 53
第四章 實驗結果 95
第五章 結論與未來展望 117

[1] B. Bross, J. Chen, J. R. Ohm, G. J. Sullivan and Y. K. Wang, “Developments in International Video Coding Standardization After AVC, with an Overview of Versatile Video Coding (VVC),” IEEE Access, Early Access, January 2021.
[2] B. Bross, K. Andersson, M. Blaser, V. Drugeon, S.-H. Kim, J. Lainema, J. Li, S. Liu, J.-R. Ohm, G. J. Sullivan, and R. Yu, ‘‘General Video Coding Technology in Responses to the Joint Call for Proposals on Video Compression with Capability Beyond HEVC,’’ IEEE Trans. Circuits Syst. Video Technol., vol. 30, no. 5, pp. 1226-1240, May 2020.
[3] J. Chen, M. Karczewicz, Y.-W. Huang, K. Choi, J.-R. Ohm, and G. J. Sullivan, ‘‘The Joint Exploration Model (JEM) for Video Compression with Capability Beyond HEVC,’’ IEEE Trans. Circuits Syst. Video Technol., vol. 30, no. 5, pp. 1208-1225, May 2020.
[4] B. Bross, J. Chen, S. Liu, and Y.-K. Wang, “Versatile Video Coding (Draft 10),” Doc. JVET-S2001, October 2020.
[5] J. Chen, Y. Ye, and S. Kim, “Algorithm Description for Versatile Video Coding and Test Model 10 (VTM 10),” Doc. JVET-S2002-v2, October 2020.
[6] G. J. Sullivan, J. R. Ohm, W. J. Han, and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) Standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1649-1668, December 2012.
[7] J. R. Ohm, G. J. Sullivan, H. Schwarz, T. K. Tan, and T. Wiegand, “Comparison of the coding efficiency of video coding standards- Including high efficiency video coding (HEVC),” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1669-1684, December 2012.
[8] T. K. Tan, R. Weerakkody, M. Mrak, N. Ramzan, V. Baroncini, J. R. Ohm, and G. J. Sullivan, “Video Quality Evaluation Methodology and Verification Testing of HEVC Compression Performance,” IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 1, pp. 76-90, January 2016.
[9] C. Rosewarne, B. Bross, M. Naccari, K. Sharman, and G. J. Sullivan, “High Efficiency Video Coding (HEVC) Test Model 16 (HM 16) Update 4 of Encoder Description,” Doc. JCTVC-V1002, October 2015.
[10] A. Segall, E. François, W. Husak, S. Iwamura, and D. Rusanovskyy, “JVET Common Test Conditions and Evaluation Procedures for HDR/WCG Video,” Doc. JVET-S2011, October 2020.
[11] A. Ortega and K. Ramchandran, “Rate Distortion Methods for Image and Video Compression,” IEEE Signal Processing Magazine, vol. 15, no. 6, pp. 23-50, November 1998.
[12] J. L. Lin, Y. W. Chen, Y. W. Huang, and S.M. Lei, “Motion Vector Coding in the HEVC Standard,” IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 6, pp. 957-968, December 2013.
[13] A. Mercat, A. Mäkinen, J. Sainio, A. Lemmetti, M. Viitanen and J. Vanne, “Comparative Rate-Distortion-Complexity Analysis of VVC and HEVC Video Codecs,” IEEE Access, vol. 9, pp. 67813-67828, May 2021.
[14] https://newsletter.fraunhofer.de/-viewonline2/17386/465/13/14SHcBTt/u8far30f3W/1 (accessed on July 6, 2020)
[15] S. H. Park and J. W. Kang, “Fast Affine Motion Estimation for Versatile Video Coding (VVC) Encoding,” IEEE Access, vol. 7, pp. 158075-158084, October 2019.
[16] H. Liu, L. Zhang, K. Zhang, H. C. Chuang, Y. Wang and J. Xu, “Two-Pass Bi-Directional Optical Flow via Motion Vector Refinement,” in Proceedings of 2019 IEEE International Conference on Image Processing, Taipei, Taiwan, September 2019.
[17] https://vcgit.hhi.fraunhofer.de/jvet/VVCSoftware_BMS/tags/ BMS-2.1 (accessed on September 11, 2018)
[18] T. Fu, H. Zhang and H. Chen, “Two-Stage Fast Multiple Transform Selection Algorithm for VVC Intra Coding,” in Proceedings of 2019 IEEE International Conference on Multimedia and Expo, Shanghai, China, July 2019.
[19] N. Tang, J. Cao, F. Liang, J. Wang, H. Liu, X. Wang, and X. Du, “Fast CTU Partition Decision Algorithm for VVC Intra and Inter Coding,” in Proceedings of 2019 IEEE Asia Pacific Conference on Circuits and Systems, Bangkok, Thailand, November 2019.
[20] T. Amestoy, A. Mercat, W. Hamidouche, D. Menard, and C. Bergeron, “Tunable VVC Frame Partitioning Based on Lightweight Machine Learning,” IEEE Transactions on Image Processing, vol. 29, pp. 1313-1328, April 2020.
[21] R. H. Hong, M. J. Chen, Y. S. Ciou, C. M. Yang, and C. H. Yeh, “Fast Inter Prediction Algorithm based on Machine Learning for H.266/VVC,” in Proceedings of the 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, August 2020.
[22] Y. Fan, J. A. Chen, H. Sun, J. Katto, and M. E. Jing, “A Fast QTMT Partition Decision Strategy for VVC Intra Prediction,” IEEE Access, vol. 8, pp. 107900-107911, June 2020.
[23] S. Peng, Z. Peng, Y. Ren, and F. Chen, “Fast Intra-Frame Coding Algorithm for Versatile Video Coding Based on Texture Feature,” in Proceedings of 2019 IEEE International Conference on Real-time Computing and Robotics, Irkutsk, Russia, August 2019.
[24] J. Cui, T. Zhang, C. Gu, X. Zhang, and S. Ma, “Gradient-Based Early Termination of CU Partition in VVC Intra Coding,” in Proceedings of 2020 Data Compression Conference, Snowbird, UT, USA, March 2020.
[25] Q. Zhang, Y. Zhao, B. Jiang, L. Huang, and T. Wei, “Fast CU Partition Decision Method Based on Texture Characteristics for H.266/VVC,” IEEE Access, vol. 8, pp. 203516-203524, November 2020.
[26] Q. Zhang, Y. Wang, L. Huang, and B. Jiang, “Fast CU Partition and Intra Mode Decision Method for H.266/VVC,” IEEE Access, vol. 8, pp. 117539-117550, June 2020.
[27] H. Yang, L. Shen, X. Dong, Q. Ding, P. An, and G. Jiang, “Low-Complexity CTU Partition Structure Decision and Fast Intra Mode Decision for Versatile Video Coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 6, pp 1668-1682, June 2020.
[28] S. H. Park and J. Kang, “Fast Multi-type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network,” IEEE Transactions on Multimedia, Early Access, December 2020.
[29] S. H. Yang and S. J. Hsiao, “H.266/VVC Fast Intra Prediction Using Sobel Edge Features,” Electronics Letters, vol. 57, no. 1, pp. 11-13, January 2021.
[30] J. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no.6, pp. 679-698, November 1986.
[31] I. Bogun and E. Riberiro, “Object-Aware Tracking,” in Proceedings of 23rd International Conference on Pattern Recognition, Cancun, Mexico, December 2016.
[32] G. Bjontegaard, “Calculation of Average PSNR Differences between RD Curves,” ITU-T SG16/Q6 Document, VCEG-M33, Austin, April 2001.
[33] G. Bjontegaard, “Improvements of the BD-PSNR model,” ITU-T SG16/Q6, Document, VCEG-AI11, Berlin, July 2008.
[34] W. Zhou, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: from Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.