面向VVC标准的CU划分模式快速决策算法综述
A Review of Fast Decision Algorithms for CU Partitioning Patterns for VVC Standard
DOI: 10.12677/sea.2024.133044, PDF,    科研立项经费支持
作者: 胡译夙, 郑钧文, 王 滢：浙江万里学院信息与智能工程学院，浙江 宁波
关键词: 视频编码；通用视频编码；划分模式；编码单元；Video Coding； Versatile Video Coding； Partitioning Patterns； Coding Unit
摘要: 视频编码是数字视频传输和存储的核心技术之一。随着移动互联网和视频流媒体的快速发展，视频编码技术面临着越来越多的挑战。通用视频编码(Versatile Video Coding, VVC)是新一代视频编码标准，它提供了更高的压缩效率来满足日益增长的视频传输和存储需求。编码单元(Coding Unit, CU)划分是VVC编码中的一个重要步骤，用于将视频帧划分为不同大小的块，以便进行不同的编码和处理操作。开发快速而准确的CU划分算法对于提高编码效率至关重要。本文综述了面向VVC标准的快速CU划分算法，分机器学习(ML)下的算法和其他算法两大类进行归纳分析，并探讨了它们各自的优缺点。最后，对CU快速划分算法进行了简单总结和展望。
Abstract: Video coding is a fundamental technology for digital video transmission and storage. With the rapid growth of mobile Internet and video streaming, video coding faces increasing challenges. Versatile Video Coding (VVC) represents the new generation of video coding standards, aiming to provide higher compression efficiency to meet the rising demands of video transmission and storage. A critical step in VVC coding is the Coding Unit (CU) partitioning, which involves partitioning the video frame into differently sized chunks for various coding and processing operations. The development of a fast and accurate CU partitioning algorithm is pivotal in enhancing coding efficiency. In this paper, we review fast CU partitioning algorithms for the VVC standard, categorizing them into two groups: algorithms based on Machine Learning (ML) and other algorithms. We also discuss their respective advantages and disadvantages. In conclusion, we provide a brief summary and future prospect for CU fast partitioning algorithms.
文章引用：胡译夙, 郑钧文, 王滢. 面向VVC标准的CU划分模式快速决策算法综述[J]. 软件工程与应用, 2024, 13(3): 424-438. https://doi.org/10.12677/sea.2024.133044

参考文献

[1] Jing, Z., Li, P., Zhao, J. and Zhang, Q. (2022) A Fast CU Partition Algorithm Based on Gradient Structural Similarity and Texture Features. Symmetry, 14, Article 2644. [Google Scholar] [CrossRef] 
[2] Zhang, S., Feng, S., Chen, J., Zhou, C. and Yang, F. (2022) A Gcn-Based Fast CU Partition Method of Intra-Mode VVC. Journal of Visual Communication and Image Representation, 88, Article ID: 103621. [Google Scholar] [CrossRef] 
[3] Liu, H., Zhu, S., Xiong, R., Liu, G. and Zeng, B. (2021) Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding. 2021 International Conference on Visual Communications and Image Processing (VCIP), Munich, 5-8 December 2021, 1-5. [Google Scholar] [CrossRef] 
[4] Fang, J., Liu, B. and Chang, P. (2022) Fast Coding Unit Partitioning Algorithms for Versatile Video Coding Intra Coding. Journal of Visual Communication and Image Representation, 87, Article ID: 103542. [Google Scholar] [CrossRef] 
[5] Li, H.C., Zhang, P., Jin, B.H. and Zhang, Q.W. (2023) Fast CU Decision Algorithm Based on Texture Complexity and CNN for VVC. IEEE Access, 11, 35808-35817. [Google Scholar] [CrossRef] 
[6] Xu, J., Wu, G., Zhu, C., Huang, Y. and Song, L. (2022). CNN-Based Fast CU Partitioning Algorithm for VVC Intra Coding. 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, 16-19 October 2022, 2706-2710.[CrossRef] 
[7] Ding, G., Lin, X., Wang, J. and Ding, D. (2023) Accelerating QTMT-Based CU Partition and Intra Mode Decision for Versatile Video Coding. Journal of Visual Communication and Image Representation, 94, Article ID: 103832. [Google Scholar] [CrossRef] 
[8] Zhang, C., Yang, W. and Zhang, Q. (2023) Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information. Electronics, 12, Article 1967.
[9] Li, H., Zhang, P., Jin, B. and Zhang, Q. (2023) Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC. Electronics, 12, Article 3053. [Google Scholar] [CrossRef] 
[10] Li, Y., Luo, F. and Zhu, Y. (2022) Temporal Prediction Model-Based Fast Inter CU Partition for Versatile Video Coding. Sensors, 22, Article 7741. [Google Scholar] [CrossRef] [PubMed]
[11] Wang, K., Liang, H., Zhang, S. and Yang, F. (2022). Fast CU Partition Method Based on Extra Trees for VVC Intra Coding. 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP), Suzhou, 13-16 December 2022, 1-5.[CrossRef] 
[12] Zhao, J., Wang, Y., Li, M. and Zhang, Q. (2022) Fast Coding Unit Size Decision Based on Deep Reinforcement Learning for Versatile Video Coding. Multimedia Tools and Applications, 81, 16371-16387. [Google Scholar] [CrossRef] 
[13] Shang, X., Li, G., Zhao, X., Han, H. and Zuo, Y. (2023) Fast CU Size Decision Algorithm for VVC Intra Coding. Multimedia Tools and Applications, 82, 28301-28322. [Google Scholar] [CrossRef] 
[14] Wei, G., Wang, X. and Zhang, T.J. (2023) Massively Accelerating VVC Intra Encoding Through Decoder-Side Neural Quality Enhancement. 2023 4th Information Communication Technologies Conference (ICTC), Nanjing, 17-19 May 2023, 407-411.
[15] Wang, Y., Liu, Y., Zhao, J. and Zhang, Q. (2023) Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets. IEEE Access, 11, 56812-56821. [Google Scholar] [CrossRef] 
[16] Imen, W., Amna, M., Fatma, B., Ezahra, S.F. and Masmoudi, N. (2022) Fast HEVC Intra-Cu Decision Partition Algorithm with Modified LeNet-5 and AlexNet. Signal, Image and Video Processing, 16, 1811-1819. [Google Scholar] [CrossRef] 
[17] Sun, Z., Yu, L. and Peng, W. (2023) QTMT-LNN: A Fast Intra CU Partition Using Lightweight Neural Network for 360‐Degree Video Coding on VVC. IET Image Processing, 17, 597-612.
[18] Das, T., Choi, K. and Choi, J. (2023) High Quality Video Frames from VVC: A Deep Neural Network Approach. IEEE Access, 11, 54254-54264. [Google Scholar] [CrossRef] 
[19] Seltsam, P., Das, P. and Wien, M. (2023). Adaptive and Scalable Compression of Multispectral Images Using VVC. 2023 Data Compression Conference (DCC), Snowbird, 21-24 March 2023, 361.[CrossRef] 
[20] Hamidouche, W., Biatek, T., Abdoli, M., Francois, E., Pescador, F., Radosavljevic, M., et al. (2022) Versatile Video Coding Standard: A Review from Coding Tools to Consumers Deployment. IEEE Consumer Electronics Magazine, 11, 10-24. [Google Scholar] [CrossRef] 

为你推荐