Skip to main content
Log in

Edge-aware coding tree unit hierarchical partitioning for quality scalable compression of depth maps

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

The scalable extension of the high efficiency video coding standard (SHVC) combines the large compression efficiency and high visual quality of HEVC, with the possibility of encoding different versions of the same video in a single bitstream. However, this comes at the cost of high computational complexity. In this context, many research works aim to reduce this complexity for texture images. We aim at achieving the same objective, but for depth maps whose characteristics make them different from conventional texture images. Depth maps are indeed characterized by areas of smoothly varying grey levels separated by sharp discontinuities at object boundaries. Preserving these discontinuities is crucial to enable high quality of synthesized views at the receiver side. In this paper, we propose a fast depth maps encoding scheme for quality scalable HEVC while exploiting depth maps characteristics, SHVC Coding Unit (CU) depth information and the correlation between the Base Layer (BL) and the Enhancement Layers (ELs) of SHVC. If a CU corresponds to a depth smooth region, we maintain the same best coding depth of its co-located in the BL. If a CU is a sharp region, the best coding depth is computed in the same way as in the original SHVC. Experiments are conducted and satisfying results are obtained as the proposed method improves the SHVC coding speed without a significant impact on the synthesized views Rate-Distortion tradeoff

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

Access this article

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans. Circuits Syst. Video Technol. 17(9), 1103–1120 (2007)

    Article  Google Scholar 

  2. Daerawi, Ramli, K., Rantelobo, K.: Performance Evaluation of Scalable High Efficiency Video Coding (SHVC) Transmissions, in International Conference on Science and Technology (ICST), Yogyakarta, Indonesia, August 2018, pp. 1–6

  3. Boyce, J.M., Ye, Y., Chen, J., Ramasubramonian, A.K.: Overview of SHVC: scalable extensions of the high efficiency video coding standard. IEEE Trans. Circuits Syst. Video Technol. 26(1), 20–34 (2016)

    Article  Google Scholar 

  4. Kim, W.-S., Ortega, A., Lai, P., Tian, D., Gomila, C.: Depth map distortion analysis for view rendering and depth coding, in IEEE international conference on image processing (ICIP), pp. 721–724. Egypt, November, Cairo (2009)

  5. Bailleul, R., Cock, J.D., Walle, R.V.D.: Fast Mode Decision for SNR Scalability in SHVC Digest of Technical Papers, in IEEE International Conference on Consumer Electronics (ICCE), 2014

  6. Wang, C.-C., Chang, Y.-S., Huang, K.-N.: Efficient Coding Tree Unit (CTU) Decision Method for Scalable High-Efficiency Video Coding (SHVC) Encoder, in Recent Advances in Image and Video Coding, 2016

  7. Li, X., Chen, M., Qu, Z., Xiao, J., Gabbouj, M.: An effective CU size decision method for quality scalability in SHVC. Multimed. Tools Appl. 76, 8011–8030 (2017)

    Article  Google Scholar 

  8. Li, Q., Liu, B., Wang, D.: Fast CU size decision and PU mode decision algorithm for quality SHVC inter coding. Multimed. Tools Appl. 78, (2018)

  9. Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep Multi-View Enhancement Hashing for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)

  10. Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3D room layout estimation from a single RGB image. IEEE Trans. Multimed. 22(11), 3014–3024 (2020)

    Article  Google Scholar 

  11. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model, ACM Transactions on Multimedia Computing Communications and Applications, vol.16, no.4, 2020

  12. Merkle, P., Smolic, A., Muller, K., Wiegand, T.: Multi-view video plus depth representation and coding , in International Conference on Image Processing, 2007

  13. Sehoon, Y., Anthony, V.: Multi-layered coding of depth for virtual view synthesis, in Picture Coding Symposium, 2009

  14. Schwarz, H., Bartnik, C., Bosse, S., Brust, H., Hinz, T., Lakshman, H., Marpe, D., Merkle, P., Muller, K., Rhee, H., Tech, G., Winken, M., Wiegand, T.: 3D Video Coding Using Advanced Prediction, Depth Modeling, and Encoder Control Methods, in Picture Coding Symposium, pp. 1–4. Poland, May, Kraków (2012)

  15. Ying, C., Karsten, M., Jens-Rainer, O., Anthony, V., Ye-Kui, W.: Overview of the multiview and 3D extensions of high efficiency video coding. IEEE Trans. Circuits Syst. Video Technol. 26(1), 35–49 (2015)

    Google Scholar 

  16. Scharstein, D., Szeliski, R., Zabih, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(7), 7–42 (2002)

    Article  Google Scholar 

  17. SHM software repository, https://hevc.hhi.fraunhofer.de/svn/ svn_SHVCSoftware / tags/ SHM-12.0/

  18. 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)

    Article  Google Scholar 

  19. Bjontegaard, G.: Calculation of average PSNR differences between RD-curves, in Technical Report VCEG-M33, ITU-T SG16/Q6, 2001

  20. Sebai, D.: Performance analysis of HEVC scalable extension for depth maps. J Signal Process Syst 92(7), 747–761 (2020)

    Article  Google Scholar 

  21. Jing, H., He, X., Han, Q., El-Latif, A.A.A., Niu, X.: Saliency detection based on integrated features. Neurocomputing 129, 114–121 (2014)

    Article  Google Scholar 

  22. Shi, Z., Yu, L., El-Latif, A.A.A., Niu, X.: Skeleton modulated topological perception map for rapid viewpoint selection. IEICE Trans. Inform. Syst. 10, 2585–2588 (2012)

    Article  Google Scholar 

  23. Bai, X., Zhang, T., Wang, C., El-Latif, A.A.A., Niu, X.: A fully automatic player detection method based on one-class SVM. IEICE Trans. Inform. Syst. 2, 387–391 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dorsaf Sebai.

Additional information

Communicated by Y. Zhang.

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

Sebai, D., Mosbah, S. & Ghorbel, F. Edge-aware coding tree unit hierarchical partitioning for quality scalable compression of depth maps. Multimedia Systems 27, 893–906 (2021). https://doi.org/10.1007/s00530-021-00766-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-021-00766-w

Keywords

Navigation