Skip to main content

Abstract

This chapter provides background about Three-Dimensional High Efficiency Video Coding (3D-HEVC) depth map encoding. Initially, the 3D-HEVC encoding structure is defined, relating its frame to a quadtree-like structure. The algorithms used in the intra-frame prediction of depth maps are described in detail, allowing a comparative understanding regarding the predictability of texture coding. Next, inter-frame and inter-view prediction algorithms are presented, followed by the description of standard tools used in the intra-frame, inter-frame, and inter-view predictions. This chapter finishes by describing the Common Test Conditions (CTC), which must be followed to generate results comparable to other related works.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Saldanha, M., G. Sanchez, C. Marcon, and L. Agostini. 2019. Fast 3D-HEVC depth maps encoding using machine learning. IEEE Transactions on Circuits and Systems for Video Technology 1–1: 12.

    Google Scholar 

  2. Marpe, D., H. Schwarz, S. Bosse, B. Bross, P. Helle, T. Hinz, H. Kirchhoffer, H. Lakshman, T. Nguyen, S. Oudin, et al. 2010. Video compression using nested quadtree structures, leaf merging, and improved techniques for motion representation and entropy coding. IEEE Transactions on Circuits and Systems for Video Technology 20–12: 1676–1687.

    Article  Google Scholar 

  3. Sullivan, G.J., J.-R. Ohm, W.-J. Han, T. Wiegand, et al. 2012. Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on circuits and systems for video technology 22 (12): 1649–1668.

    Article  Google Scholar 

  4. ITU-T VCEG and ISO/IEC MPEG. 2017. 3D-HEVC Test Model. Source: https://hevc.hhi.fraunhofer.de/svn/svn_3DVCSoftware/tags/HTM-16.0/, Sep 2017.

  5. Merkle, P., K. Müller, D. Marpe, and T. Wiegand. 2016. Depth intra coding for 3D video based on geometric primitives. IEEE Transactions on Circuits and Systems for Video Technology 26–3: 570–582.

    Article  Google Scholar 

  6. Lee, J., M. Park, and C. Kim. 2015. 3d-ce1: depth intra skip (dis) mode, Technical Report, ISO/IEC JTC1/SC29/WG11, 5.

    Google Scholar 

  7. Liu, H., and Y. Chen. 2014. Generic segment-wise DC for 3D-HEVC depth intra coding. IEEE International Conference on Image Processing: 3219–3222.

    Google Scholar 

  8. Lainema, J., F. Bossen, W.-J. Han, J. Min, and K. Ugur. 2012. Intra coding of the HEVC standard. IEEE Transactions on Circuits and Systems for Video Technology 22 (12): 1792–1801.

    Article  Google Scholar 

  9. Zhao, L., L. Zhang, S. Ma, and D. Zhao. 2011. Fast mode decision algorithm for intra prediction in HEVC. IEEE Visual Communications and Image Processing: 1–4.

    Google Scholar 

  10. Tech, G., H. Schwarz, K. Müller, and T. Wiegand. 2012. 3D video coding using the synthesized view distortion change. Picture Coding Symposium: 25–28.

    Google Scholar 

  11. Müller, K., H. Schwarz, D. Marpe, C. Bartnik, S. Bosse, H. Brust, T. Hinz, H. Lakshman, P. Merkle, F.H. Rhee, et al. 2013. 3D high-efficiency video coding for multi-view vídeo and depth data. IEEE Transactions on Image Processing 22 (9): 3366–3378.

    Article  MathSciNet  Google Scholar 

  12. Cheng, Y.-S., Z.-Y. Chen, and P.-C. Chang. 2009. An H.264 spatio-temporal hierarchical fast motion estimation algorithm for high-definition video. IEEE International Symposium on Circuits and Systems: 880–883.

    Google Scholar 

  13. Tang, X.-l., S.-K. Dai, and C.-H. Cai. 2010. An analysis of TZSearch algorithm in JMVC. International Conference on Green Circuits and Systems: 516–520.

    Google Scholar 

  14. Winken, M., P. Helle, D. Marpe, H. Schwarz, and T. Wiegand. 2011. Transform coding in the HEVC test model. IEEE International Conference on Image Processing: 3693–3696.

    Google Scholar 

  15. Budagavi, M., A. Fuldseth, and G. Bjontegaard. 2014. High efficiency video coding (HEVC): Algorithms and architectures. Vol. 6, 141–169. Cambridge: Springer.

    Book  Google Scholar 

  16. Marpe, D., H. Schwarz, and T. Wiegand. 2003. Context-based adaptive binary arithmetic coding in the H. 264/AVC video compression standard. IEEE Transactions on circuits and systems for video technology 13–7: 620–636.

    Article  Google Scholar 

  17. Müller. K, and A. Vetro. 2014. Common test conditions of 3DV Core experiments, Technical Report, ISO/IEC JTC1/SC29/WG11, 7p.

    Google Scholar 

  18. Tanimoto Lab NICT. 2017. National Institute of Information and Communication Technology. Source: http://www.tanimoto.nuee.nagoya-u.ac.jp/, Sep 2017.

  19. Ho, Y.-S., E.-K. Lee, C. Lee. 2008. M15419, multiview video test sequence and camera parameters, Technical Report, ISO/IEC JTC1/SC29/WG11, 6p.

    Google Scholar 

  20. Zhang, J.; Li, R.; Li, H.; Rusanovskyy, D.; Hannuksela, M. M.. 2011. Ghost Town Fly 3DV sequence for purposes of 3DV standardization, Technical Report, ISO/IECJTC1/SC29/WG11, 5p.

    Google Scholar 

  21. Domañski, M., T. Grajek, K. Klimaszewski, M. Kurc, O. Stankiewicz, J. Stankowski, and K. Wegner. 2009. Poznan multiview video test sequences and camera parameters, Technical Report, ISO/IEC JTC1/SC29/WG11, 6.

    Google Scholar 

  22. Rusanovskyy D., P. Aflaki, and M. Hannuksela. 2011. Undo Dancer 3DV sequence for purposes of 3DV standardization, Technical Report, ISO/IEC JTC1/SC29/WG11, 6.

    Google Scholar 

  23. NICT. 2017. National Institute of Information and Communication Technology. Source: ftp://ftp.merl.com/pub/tian/NICT-3D/Shark/.

  24. Bjontegaard, G. 2001. Calculation of average PSNR differences between RD-curves, Technical Report, ITU-T SC16/SG16 VCEG-M33, 4.

    Google Scholar 

  25. Chen, Y, G. Tech, K. Wegner, and S. Yea. 2015. Test model 11 of 3D-HEVC and MV-HEVC, Technical Report, ISO/IEC JTC1/SC29/WG11, 58.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sanchez, G., Agostini, L., Marcon, C. (2020). 3D-HEVC Background. In: Algorithms for Efficient and Fast 3D-HEVC Depth Map Encoding. Springer, Cham. https://doi.org/10.1007/978-3-030-25927-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-25927-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25926-6

  • Online ISBN: 978-3-030-25927-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics