Skip to main content
SpringerLink
Log in

A fast CU size decision algorithm for VVC intra prediction based on support vector machine

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The latest generation of coding standard, Versatile Video Coding (VVC), has achieved more bitrate reduction compared with high efficiency video coding. However, the introduction of quadtree with nested Multi-Type Tree (MTT) coding structure greatly increases the computational complexity. To reduce the complexity of VVC, a Support Vector Machine (SVM) based Coding Unit (CU) size decision algorithm is presented. Firstly, effective features, derived from entropy, texture contrast, and Haar wavelet efficient of current CU, are select to distinguish the splitting directions. Then, the six SVM classifying models are on-line trained at different CU sizes. Finally, the models are utilized to prediction the direction of CU splitting in the quadtree with nested MTT coding structure. Experimental results show that the proposed algorithm can significantly save the encoding time by 51.01% with slight increase of Bjontegaard delta bit rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Bjontegaard G (2001) Calculation of average PSNR differences between RD curves, ITU-T SG16 Q6, VCEG-M33

  2. J. Boyce, K. Suehring, X. Li, et al (2018) JVET common test conditions and software reference configurations, JVET-J1010, Joint Vid Explor Team

  3. Bross B (2018) Versatile video coding (Draft 1), JVET-J1001-v2, Joint Vid Exp Team (JVET)

  4. Y. Chen and C. Lin (2006) Combining SVMs with various feature selection strategies. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 315–324

  5. J. Chen, E. Alshina, G.-J. Sullivan, et al (2015) Algorithm description of joint exploration test model 1, JEVT-A1001, Joint Vid Explor Team (JVET)

  6. J. Chen, Y. Chen, M Karczewicz, et al (2015) Coding tools investigation for next generation video coding, ITU-T SG16 Doc.COM16-C806-E

  7. Cho S, Kim M (2013) Fast CU splitting and pruning for suboptimal CU partitioning in HEVC intra coding. IEEE Trans Circ Syst Vid Technol 23:1555–1564

    Article  Google Scholar 

  8. Feng Z, Liu P, Jia K, Duan K (2018) Fast intra CTU depth decision for HEVC. IEEE Access 6:45262–45269

    Article  Google Scholar 

  9. T. Fu, H. Zhang, F. Mu, H. Base (2019) Fast CU partitioning algorithm for H.266/VVC intra-frame coding, IEEE International Conference on Multimedia and Expo (ICME), pp 55–60

  10. Grellert M, Zatt B, Bampi S, da Silva Cruz LA (2019) Fast coding unit partition decision for HEVC using support vector machines. IEEE Trans Circ Syst Vid Technol 29:1741–1753

    Article  Google Scholar 

  11. C. Hsu, C. Chang, C. Lin (2003) A practical guide to support vector classification, Tech Rep (Department of Computer Science, National Taiwan University). http://www.csie.ntu.edu.tw/cjlin/guide/guide.pdf, Accessed 27 June 2019

  12. H. Huang, K. Zhang, Y. Huang, et al (2016) EE2.1: Quadtree plus binary tree structure integration with JEM tools, JVET-C0024, Joint Vid Explor Team

  13. Huang C, Peng Z, Chen F, Jiang Q, Jiang G, Hu Q (2018) Efficient CU and PU decision based on neural network and gray level co-occurrence matrix for intra prediction of screen content coding. IEEE Access 6:46643–46655

    Article  Google Scholar 

  14. Kim K, Ro WW (2019) Fast CU depth decision for HEVC using neural networks. IEEE Trans Circ Syst Vid Technol 29:1462–1473

    Article  Google Scholar 

  15. Lee D, Jeong J (2017) Fast intra coding unit decision for high efficiency video coding based on statistical information. Signal Proc Image Commun 55:121–129

    Article  Google Scholar 

  16. Lin TL, Jiang HY, Huang JY et al (2018) Fast intra coding unit partition decision in H.266/FVC based on spatial features. J Real-Time Image Process 0:1–18

    Google Scholar 

  17. Liu X, Li Y, Liu D et al (2017) An adaptive CU size decision algorithm for HEVC intra prediction based on complexity classification using machine learning. IEEE Trans Circ Syst Vid Technol 29(1):144–155

    Article  MathSciNet  Google Scholar 

  18. Ruiz D, Fernández-Escribano G, Adzic V, Kalva H, Martínez JL, Cuenca P (2017) Fast CU partitioning algorithm for HEVC intra coding using data mining. Multimed Tools Appl 76:861–894

    Article  Google Scholar 

  19. Ryu S, Kang J (2018) Machine learning-based fast angular prediction mode decision technique in video coding. IEEE Trans Image Process 27:5525–5538

    Article  MathSciNet  Google Scholar 

  20. Shen X, Yu L (2013) CU splitting early termination based on weighted SVM. Eurasip J Image Vid Proc 2013:1–11

    Article  Google Scholar 

  21. Sullivan GJ, Ohm J-R, Han W-J, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circ Syst Vid Technol 22(12):1649–1668

    Article  Google Scholar 

  22. Sun X, Chen X, Xu Y, Wang Y, Yu D (2017) Fast CU partition strategy for HEVC based on Haar wavelet. IET Image Process 11:717–723

    Article  Google Scholar 

  23. Wang Z, Wang S, Zhang J, Wang S, Ma S (2018) Probabilistic decision based block partitioning for future video coding. IEEE Trans Image Process 27:1475–1486

    Article  MathSciNet  Google Scholar 

  24. A. Wieckowski, T. Hinz, V. George, et al (2018) AHG10: updated NextSoftware as an alternative implementation of the joint exploration model (JEM), JVET-I0034, Joint Vid Explor Team

  25. A. Wieckowski, T. Hinz, B. Bross, et al (2018) NextSoftware as test software, JVET-J0095, Joint Vid Explor Team

  26. Xu M, Li T, Wang Z, Deng X, Yang R, Guan Z (2018) Reducing complexity of HEVC: a deep learning approach. IEEE Trans Image Process 27:5044–5059

    Article  MathSciNet  Google Scholar 

  27. Yang H, Shen L, Dong X, Ding Q, An P, Jiang G (2019) Low complexity CTU partition structure decision and fast intra mode decision for versatile video coding. IEEE Trans Circ Syst Vid Technol 30:1668–1682. https://doi.org/10.1109/tcsvt.2019.2904198

    Article  Google Scholar 

  28. Zhang Y, Kwong S, Wang X et al (2015) Machine learning-based coding unit depth decisions for flexible complexity allocation in high efficiency video coding. IEEE Trans Image Process 24:2225–2238

    Article  MathSciNet  Google Scholar 

  29. Zhang M, Lai D, Liu Z, An C (2019) A novel adaptive fast partition algorithm based on CU complexity analysis in HEVC. Multimed Tools Appl 78:1035–1051

    Article  Google Scholar 

  30. Zhu L, Zhang Y, Kwong S, Wang X, Zhao T (2018) Fuzzy SVM-based coding unit decision in HEVC. IEEE Trans Broadcast 64:681–694

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61771269, 61620106012, and 61871247, the Natural Science Foundation of Zhejiang Province under No. LY20F010005, the Natural Science Foundation of Ningbo under Nos. 2018A610052 and 2019A610107, and the K. C. Wong Magna Fund in Ningbo University.

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, 400054, China

    Fen Chen & Zongju Peng

  2. Faculty of information Science and Engineering, Ningbo University, Ningbo, 315211, Zhejiang, China

    Yan Ren, Zongju Peng, Gangyi Jiang & Xin Cui

Authors
  1. Fen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zongju Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gangyi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xin Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zongju Peng.

Ethics declarations

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, F., Ren, Y., Peng, Z. et al. A fast CU size decision algorithm for VVC intra prediction based on support vector machine. Multimed Tools Appl 79, 27923–27939 (2020). https://doi.org/10.1007/s11042-020-09401-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09401-8

Keywords

Search

Navigation