ABSTRACT
Abstract: Different from the traditional quaternary tree (QT) structure utilized in the previous generation video coding standard H.265/HEVC, a new partition structure named quadtree with nested multi-type tree (QTMT) is applied in the latest codec H.266/VVC. The introduction of QTMT brings in superior encoding performance at the cost of great time-consuming. Therefore, this paper proposes a fast coding unit (CU) partitioning algorithm based on CU texture complexity and texture direction. First, we terminate further splitting of a CU when its texture is judged as simple. Then, we use the gray level co-occurrence matrix (GLCM) to extract the texture direction of the block to decide whether to partition this CU by QT, thus terminating further MT partitions. Finally, a final partition type is selected from the four MT partitions in combination with the multi-level texture complexity and texture direction of the block. The simulation results show that the overall algorithm can significantly reduce the encoding time, while the loss of coding efficiency is reasonably low. In comparison with the reference model, the encoding time is reduced by up to 44.71%, while the BDBR is increased by only 0.84% on average.
- B. Bross , "Overview of the versatile video coding (VVC) standard and its applications," IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 10, pp. 3736-3764, 2021.Google ScholarCross Ref
- S.-C. Lim, D.-Y. Kim, and J. Kang, "Simplification on Cross-Component Linear Model in Versatile Video Coding," Electronics, vol. 9, no. 11, p. 1885, 2020.Google ScholarCross Ref
- J. Chen, Y. Ye, and S. Kim, "Algorithm description for Versatile Video Coding and Test Model 12 (VTM 12)," Document: JVET-U2002-v1, Brussels, 6–15 January 2021. Available online: https://jvet-experts.org/.Google Scholar
- X. Jiang, F. R. Yu, T. Song, Z. Ma, Y. Song, and D. Zhu, "Blockchain-Enabled Cross-Domain Object Detection for Autonomous Driving: A Model Sharing Approach," IEEE Internet of Things Journal, vol. 7, no. 5, pp. 3681-3692, 2020, doi: 10.1109/JIOT.2020.2967788.Google ScholarCross Ref
- X. Jiang, F. R. Yu, T. Song, and V. C. M. Leung, "A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges," IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 871-903, 2021, doi: 10.1109/COMST.2021.3065237.Google ScholarCross Ref
- X. Jiang, F. R. Yu, T. Song, and V. C. M. Leung, "Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 5955-5975, 2022, doi: 10.1109/TITS.2021.3065209.Google ScholarDigital Library
- X. Jiang, F. R. Yu, T. Song, and V. C. M. Leung, "Edge Intelligence for Object Detection in Blockchain-Based Internet of Vehicles: Convergence of Symbolic and Connectionist AI," IEEE Wireless Communications, vol. 28, no. 4, pp. 49-55, 2021, doi: 10.1109/MWC.201.2000462.Google ScholarCross Ref
- E. Alshina and J. Chen, "Algorithm Description for Versatile Video Coding and Test Model 1 (VTM 1)," document JVET-Jl002-v2, SanDiego, US, 10–20 April 2018. Available online: https://jvet-experts.org/.Google Scholar
- L. Shen, Z. Zhang, and Z. Liu, "Effective CU size decision for HEVC intracoding," IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4232-4241, 2014.Google ScholarCross Ref
- 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, 2019.Google ScholarCross Ref
- S. Huade, L. Fan, and C. Huanbang, "A fast CU size decision algorithm based on adaptive depth selection for HEVC encoder," in 2014 International Conference on Audio, Language and Image Processing, 2014: IEEE, pp. 143-146.Google Scholar
- T. Fu, H. Zhang, F. Mu, and H. Chen, "Fast CU partitioning algorithm for H. 266/VVC intra-frame coding," in 2019 IEEE International Conference on Multimedia and Expo (ICME), 2019: IEEE, pp. 55-60.Google Scholar
- M. Xu, T. Li, Z. Wang, X. Deng, R. Yang, and Z. Guan, "Reducing complexity of HEVC: A deep learning approach," IEEE Transactions on Image Processing, vol. 27, no. 10, pp. 5044-5059, 2018.Google ScholarCross Ref
- F. Galpin, F. Racapé, S. Jaiswal, P. Bordes, F. Le Léannec, and E. François, "CNN-based driving of block partitioning for intra slices encoding," in 2019 Data Compression Conference (DCC), 2019: IEEE, pp. 162-171.Google Scholar
- S. Javaid, S. Rizvi, M. T. Ubaid, and A. Tariq, "VVC/H. 266 Intra Mode QTMT Based CU Partition Using CNN," IEEE Access, vol. 10, pp. 37246-37256, 2022.Google ScholarCross Ref
- Y. Fan, H. Sun, J. Katto, and J. Ming'E, "A fast QTMT partition decision strategy for VVC intra prediction," IEEE Access, vol. 8, pp. 107900-107911, 2020.Google ScholarCross Ref
- X. Zhou, G. Shi, and Z. Duan, "Visual saliency-based fast intracoding algorithm for high efficiency video coding," Journal of Electronic Imaging, vol. 26, no. 1, p. 013019, 2017.Google ScholarCross Ref
- B. Lohithashva, V. M. Aradhya, and D. Guru, "Violent Video Event Detection Based on Integrated LBP and GLCM Texture Features," Rev. d'Intelligence Artif., vol. 34, no. 2, pp. 179-187, 2020.Google ScholarCross Ref
- P. Mohanaiah, P. Sathyanarayana, and L. GuruKumar, "Image texture feature extraction using GLCM approach," International journal of scientific and research publications, vol. 3, no. 5, pp. 1-5, 2013.Google Scholar
- G. Bjøntegaard, "Calculation of average PSNR differences between RD-curves (2001)," Document VCEG-M33, 2018.Google Scholar
- T. Zhao, Y. Huang, W. Feng, Y. Xu, and S. Kwong, "Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation," arXiv preprint arXiv:2205.03587, 2022.Google Scholar
- G. Tang, M. Jing, X. Zeng, and Y. Fan, "Adaptive CU split decision with pooling-variable CNN for VVC intra encoding," in 2019 IEEE Visual Communications and Image Processing (VCIP), 2019: IEEE, pp. 1-4.Google Scholar
Index Terms
- A Fast CU Partitioning Algorithm Based on Texture Characteristics for VVC
Recommendations
Accelerating QTMT-based CU partition and intra mode decision for versatile video coding
AbstractThe H.266/VVC achieves about 50% bitrate saving compared to its predecessor H.265/HEVC at the expense of exponentially increased computational complexity. The most efficient but complex technique for H.266/VVC intra frame coding is the QuadTree ...
Fast ISP coding mode optimization algorithm based on CU texture complexity for VVC
AbstractIn lately published video coding standard Versatile Video Coding (VVC/ H.266), the intra sub-partitions (ISP) coding mode is proposed. It is efficient for frames with rich texture, but less efficient for frames that are very flat or constant. In ...
A fast CU partition method based on CU depth spatial correlation and RD cost characteristics for HEVC intra coding
AbstractThe High Efficiency Video Coding (HEVC) could save half of bitrates than the previous standard AVC/H.264. However, the traverse of quad-tree partition for finding the optimal CU partition structure brings high computational complexity, ...
Highlights- Spatial neighboring CUs have high correlation in CU partition depth.
- A RD cost ...
Comments