Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Fast CU partition-based machine learning approach for reducing HEVC complexity


With the development of video coding technology, the high efficiency video coding (HEVC) provides better coding efficiency compared to its predecessors H.264/AVC. HEVC improves rate distortion (RD) performance significantly with increased encoding complexity. Due to the adoption of a large variety of coding unit (CU) sizes, at RD optimization level, the quadtree partition of the CU consumes a large proportion of the encoding complexity. Hence, the computational complexity cost remains a critical issue that must be properly considered in the optimization task. In this paper, two machine learning-based fast CU partition method for inter-mode HEVC are proposed, to optimize the complexity allocation at CU level. First, we propose an online support vector machine (SVM)-based fast CU algorithm for reducing HEVC complexity. The later was trained in an online way. Second, a deep convolutional neural network (CNN) is designed to predict the CU partition, in which large-scale training database including substantial CU partition data is considered. Experimental results demonstrate that the proposed online SVM can achieve a time saving of 52.28% with a degradation of 1.928% in the bitrate (BR). However, the proposed deep CNN can reduce the encoding time by 53.99% with 0.195% BR degradation. Compared to the state-of-the art, the two proposed approaches outperform the related works in terms of both RD performance and complexity reduction at inter-mode.

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

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

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

  2. 2.

    Khemiri, R., Kibeya, H., Sayadi, F.E., Bahri, N., Atri, M., Masmoudi, N.: Optimization of HEVC motion estimation exploiting SAD and SSD GPU-based implementation. IET Image Proc. 12(2), 243–253 (2017)

  3. 3.

    Cebrián-Márquez, G., Martinez, J.L., Cuenca, P.: Adaptive inter CU partitioning based on a look-ahead stage for HEVC. Signal Process. Image Commun. 76, 97–108 (2019)

  4. 4.

    Wang, S., Luo, F., Ma, S., Zhang, X., Wang, S., Zhao, D., Gao, W.: Low complexity encoder optimization for HEVC. J. Vis. Commun. Image Represent. 35, 120–131 (2016)

  5. 5.

    Xiong, J., Li, H., Wu, Q., Meng, F.: A fast HEVC inter CU selection method based on pyramid motion divergence. IEEE Trans. Multimed. 16(2), 559–564 (2014)

  6. 6.

    Cho, S., Kim, M.: Fast CU splitting and pruning for suboptimal CU partitioning in HEVC intra coding. IEEE Trans. Circuits Syst. Video Technol. 23(9), 1555–1564 (2013)

  7. 7.

    Shen, X., Yu, L., Chen, J.: Fast coding unit size selection for HEVC based on Bayesian decision rule. In: Proceedings of Picture Coding Symposium, pp. 453–456 (2012)

  8. 8.

    Li, Y., Yang, G., Zhu, Y., Ding, X., Sun, X.: Adaptive inter CU depth decision for HEVC using optimal selection model and encoding parameters. IEEE Trans. Broadcast. 63(3), 535–546 (2017)

  9. 9.

    Fernández, D.G., Del Barrio, A.A., Botella, G., Garcia, C.: Fast and effective CU size decision based on spatial and temporal homogeneity detection. Multimed. Tools Appl. 77(5), 5907–5927 (2018)

  10. 10.

    Lee, J.H., Goswami, K., Kim, B.G., Jeong, S., Choi, J.S.: Fast encoding algorithm for high-efficiency video coding (HEVC) system based on spatio-temporal correlation. J. Real Time Image Process. 12(2), 407–418 (2016)

  11. 11.

    Ahn, Y.J., Sim, D.: Square-type-first inter-CU tree search algorithm for acceleration of HEVC encoder. J. Real Time Image Process. 12(2), 419–432 (2016)

  12. 12.

    Corrêa, G., Assuncao, P.A., Agostini, L.V., da Silva Cruz, L.A.: Fast HEVC encoding decisions using data mining. IEEE Trans. Circuits Syst. Video Technol. 25(4), 660–673 (2015)

  13. 13.

    Grellert, M., Zatt, B., Bampi, S., da Silva Cruz, L.A.: Fast coding unit partition decision for HEVC using support vector machines. IEEE Trans. Circuits Syst. Video Technol. 29, 1741–1753 (2018)

  14. 14.

    Zhang, Y., Kwong, S., Wang, X., Yuan, H., Pan, Z., Xu, L.: Machine learning-based coding unit depth decisions for flexible complexity allocation in high efficiency video coding. IEEE Trans. Image Process. 24(7), 2225–2238 (2015)

  15. 15.

    Zhu, L., Zhang, Y., Pan, Z., Wang, R., Kwong, S., Peng, Z.: Binary and multi-class learning based low complexity optimization for HEVC encoding. IEEE Trans. Broadcast. 63(3), 547–561 (2017)

  16. 16.

    Zhu, L., Zhang, Y., Kwong, S., Wang, X., Zhao, T.: Fuzzy SVM-based coding unit decision in HEVC. IEEE Trans. Broadcast. 64(3), 681–694 (2017)

  17. 17.

    Liu, Z., Yu, X., Gao, Y., Chen, S., Ji, X., Wang, D.: CU partition mode decision for HEVC hardwired intra encoder using convolution neural network. IEEE Trans. Image Process. 25(11), 5088–5103 (2016)

  18. 18.

    Hu, Q., Shi, Z., Zhang, X., Gao, Z.: Fast HEVC intra mode decision based on logistic regression classification. In: Proceedings of IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1–4 (2016)

  19. 19.

    Mallikarachchi, T., Talagala, D.S., Arachchi, H.K., Fernando, A.: Content-adaptive feature-based CU size prediction for fast low-delay video encoding in HEVC. IEEE Trans. Circuits Syst. Video Technol. 28(3), 693–705 (2018)

  20. 20.

    Amer, H., Rashwan, A., Yang, E.H.: Fully connected network for HEVC CU split decision equipped with Laplacian transparent composite model. In: Picture Coding Symposium (PCS), pp. 189–193 (2018)

  21. 21.

    Laude, T., Ostermann, J.: Deep learning-based intra prediction mode decision for HEVC. In: Proceedings of Picture Coding Symposium (PCS), pp. 1–5 (2016)

  22. 22.

    Li, T., Xu, M., Deng, X.: A deep convolutional neural network approach for complexity reduction on intra-mode HEVC. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME), pp. 1255–1260 (2017)

  23. 23.

    Wang, Y., Fan, X., Jia, C., Zhao, D., Gao, W.: Neural network based inter prediction for HEVC. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018)

  24. 24.

    Khemiri, R., Bahri, N., Belghith, F., Bouaafia, S., Sayadi, F.E., Atri, M., Masmoudi, N.: Fast Motion Estimation’s Configuration Using Diamond Pattern and ECU, CFM, and ESD, Modes for Reducing HEVC Computational Complexity, pp. 1–17. IntechOpen, “Digital Imaging” Book, London (2019)

  25. 25.

    Khemiri, R., Kibeya, H., Loukil, H., Sayadi, F.E., Atri, M., Masmoudi, N.: Real-time motion estimation diamond search algorithm for the new high efficiency video coding on FPGA. Analog Integr. Circuits Signal Process. 94, 259–276 (2018)

  26. 26.

    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

  27. 27.

    Huang, S., Cai, N., Pacheco, P.P., Narrandes, S., Wang, Y., Xu, W.: Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom. Proteom. 15(1), 41–51 (2018)

  28. 28.

    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of IEEE International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 315–323 (2011)

  29. 29.

    Xu, M., Deng, X., Li, S., Wang, Z.: Region-of-interest based conversational HEVC coding with hierarchical perception model of face. IEEE J. Sel. Top. Signal Process. 8(3), 475–489 (2014)

  30. 30.

    Ohm, J.R., Sullivan, G.J., Schwarz, H., Tan, T.K., Wiegand, T.: Comparison of the coding efficiency of video coding standards—including high efficiency video coding (HEVC). IEEE Trans. Circuits Syst. Video Technol. 22(12), 1669–1684 (2012)

  31. 31. Video Test Media. [Online]. (2017). Accessed 15 June 2019

  32. 32.

    Bossen, F.: Common test conditions and software reference configurations. Document JCTVC-L1100, Joint Collaborative Team on Video Coding, (2013)

Download references

Author information

Correspondence to Soulef Bouaafia.

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

Verify currency and authenticity via CrossMark

Cite this article

Bouaafia, S., Khemiri, R., Sayadi, F.E. et al. Fast CU partition-based machine learning approach for reducing HEVC complexity. J Real-Time Image Proc 17, 185–196 (2020).

Download citation


  • HEVC
  • Deep CNN
  • Online SVM
  • Complexity reduction