Fast intra-mode decision for depth map coding in 3D-HEVC

  • Ruyi Zhang
  • Kebin JiaEmail author
  • Pengyu Liu
  • Zhonghua Sun
Original Research Paper


3D-high efficiency video coding (3D-HEVC) contains more encoding viewpoints than traditional HEVC, resulting in a significant increase of coding complexity. In this paper, we propose a low complexity intra mode decision algorithm to reduce the number of intra modes by detecting the flat area and texture direction of the depth map. The corresponding intra prediction modes are skipped when the flat region condition is satisfied. Otherwise, the direction of the edge is detected to decrease the number of angle modes in rough mode decision, which can reduce the intra-coding complexity and coding time cost. Experimental results demonstrate that the proposed algorithm achieves on average 36.48% time saving with negligible degradation of coding performance.


3D-HEVC Depth map coding Edge detection Rough mode decision (RMD) Intra mode decision 



This paper is supported by the Project for the National Natural Science Foundation of China under Grants no. 61672064, the Beijing Natural Science Foundation under Grant no. 4172001, and Beijing Laboratory of Advanced Information Networks under Grants no. PXM2019_014204_500029.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RZ and KJ. The first draft of the manuscript was written by RZ and all authors commented on previous versions of the manuscript. The authors (PL and ZS) added after the revision of the manuscript are mainly verify the validity of the new algorithm and edit and review the revised manuscript. All authors read and approved the final manuscript.


  1. 1.
    Tech, G., Chen, Y., Muller, K., Ohm, J., Vetro, A., Wang, Y.: Overview of the multiview and 3D extensions of high efficiency video coding. IEEE Trans. Circuits Syst. Video Technol. 26(1), 35–49 (2016). CrossRefGoogle Scholar
  2. 2.
    Sullivan, G., Ohm, J., Han, W., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012). CrossRefGoogle Scholar
  3. 3.
    Muller, K., Merkle, P., Wiegand, T.: 3-D video representation using depth maps. Proc. IEEE 99(4), 643–656 (2011). CrossRefGoogle Scholar
  4. 4.
    Kauff, P., Atzpadin, N., Fehn, C., Müller, M., Schreer, O., Smolic, A.: Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability. Signal Process Image Commun 22(2), 217–234 (2007)CrossRefGoogle Scholar
  5. 5.
    Vetro, A., Chen, Y., Mueller, K.: HEVC-compatible extensions for advanced coding of 3D and multiview video. In: 2015 data compression conference, Snowbird, UT, pp. 13–22 (2015)Google Scholar
  6. 6.
    Müller, K., Schwarz, H., Marpe, D., Bartnik, C., Bosse, S., Brust, H., Hinz, T., Lakshman, H., Merkle, P., Rhee, H., Tech, G., Winken, M., Wiegand, T.: 3D high-efficiency video coding for multi-view video and depth data. IEEE Trans. Image Process. 22(9), 3366–3378 (2013). MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Schwarz, H., Bartnik, C., Bosse, S., Brust, H., Hinz, T., Lakshman, H., Merkle, P., Müller, K., Rhee, H., Tech, G., Winken, M., Marpe, D., Wiegand, T.: Extension of high efficiency video coding (HEVC) for multiview video and depth data. In: IEEE international conference on image processing, Orlando, Florida, USA, pp. 205–208 (2012)Google Scholar
  8. 8.
    Saldanha, M., Sanchez, G., Zatt, B., Porto, M., Agostini L.: Complexity reduction for the 3D-HEVC depth maps coding. IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal, 2015, pp. 621-624,
  9. 9.
    Park, C.: Edge-based intramode selection for depth-map coding in 3D-HEVC. IEEE Trans. Image Process. 24(1), 155–162 (2015). MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Zhang, Q., Huang, K., Wang, X., Jiang, B., Gan, Y.: Efficient multiview video plus depth coding for 3D-HEVC based on complexity classification of the treeblock. J. Real-Time Image Process 4, 1–18 (2017). CrossRefGoogle Scholar
  11. 11.
    Li, T., Yu, L., Wang, S., Wang, H.: Simplified depth intra coding based on texture feature and spatial correlation in 3D-HEVC. In: 2018 data compression conference, Snowbird, UT, pp. 421–421 (2018)Google Scholar
  12. 12.
    Irwin, S.: An isotropic 3 × 3 image gradient operator. Presentation at Stanford A.I. Project 1968 (2014)Google Scholar
  13. 13.
    Marpe, D., Schwarz, H., Bosse, S., Bross, B., Helle, P., Hinz, T., Kirchhoffer, H., Lakshman, H., Nguyen, T., Oudin, S., Siekmann, M., Suhring, K., Winken, M., Wiegand, T.: Video compression using nested quadtree structures, leaf merging, and improved techniques for motion representation and entropy coding. IEEE Trans. Circuits Syst. Video Technol. 20(12), 1676–1687 (2010)CrossRefGoogle Scholar
  14. 14.
    Lainema, J., Ugur, K., Bici, O.: Planar intra coding for improved subjective video quality. In: Document JCTVC-D326, 4th JVT-VC meeting, Daegu, KR. [Online]. (2011)
  15. 15.
    Liu, P., He G., Xue, S. Li, Y.: A fast mode selection for depth modelling modes of intra depth coding in 3D-HEVC. In: Visual communications and image processing (VCIP), Chengdu, China, pp. 1–4 (2016)Google Scholar
  16. 16.
    Sanchez, G., Agostini, L., Marcon, C.: Complexity reduction by modes reduction in RD-list for intra-frame prediction in 3D-HEVC depth maps. In: IEEE international symposium on circuits and systems (ISCAS), Baltimore, Maryland, pp. 1–4 (2017)Google Scholar
  17. 17.
    Zhao, L., Zhang, L., Zhao, X., Ma, S., Zhao, D., Gao, W.: Further encoder improvement of intra mode decision. In: Document JCTVC-D283, 4th JVT-VC meeting, Daegu, KR. [Online]. Available: (2011)
  18. 18.
    Zhang, H., Fu, C., Chan, Y., Tsang, S., Siu, W.: Probability-based depth intra-mode skipping strategy and novel VSO metric for DMM decision in 3D-HEVC. IEEE Trans Circuits Syst Video Technol 28(2), 513–527 (2018)CrossRefGoogle Scholar
  19. 19.
    Boseen. F.: 3D-HEVC software HTM16.0 [Online]. (2015)
  20. 20.
    Dong, X., Li, M., Miao, J., Wang, Z.: Edge detection operator for underwater target image. In: 2018 IEEE 3rd international conference on image, vision and computing (ICIVC), Chongqing, pp. 91–95 (2018)Google Scholar
  21. 21.
    Long, X., Wu, X., Yang, X.: Remaining edges linking method of motion segmentation based on edge detection. In: 2012 9th international conference on fuzzy systems and knowledge discovery, Sichuan, pp. 1895–1899 (2012)Google Scholar
  22. 22.
    Mueller, K., Vetro, A.: Common test conditions of 3DV core experiments. In: Document JCT3V-G1100, 7th JVT-3V meeting, San José [Online]. (2014)
  23. 23.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
  24. 24.
    Bjøntegaard, G.: Calculation of average PSNR differences between RD curves. In: Document VCEG-M33, 13th ITU-T SG16/Q6 VCEG meeting, Austin, Texas, USA (2001)Google Scholar
  25. 25.
    Song, Y., Jia K., Wu, Q.: Low complexity texture mode decision method for 3D-HEVC. In: 2015 IEEE international conference on signal processing, communications and computing (ICSPCC). pp. 1–4. Ningbo (2015)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Ruyi Zhang
    • 1
    • 2
  • Kebin Jia
    • 1
    • 2
    Email author
  • Pengyu Liu
    • 1
    • 2
  • Zhonghua Sun
    • 1
    • 2
  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing University of TechnologyBeijingChina

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