Advertisement

Fast 3D-HEVC PU size decision algorithm for depth map intra-video coding

  • Hamza HamoutEmail author
  • Abderrahmane Elyousfi
Original Research Paper
  • 4 Downloads

Abstract

High-Efficiency Video Coding (HEVC)-based 3D video coding (3D-HEVC) is the most recent standard and last exertion of ISO/IEC MPEG and ITU-T Video Coding Experts Group (VCEG) for 3D video coding using a new data video format called Multi-View Video plus Depth map (MVD). This new standard achieves a high coding improvement. In any case, one of the most critical difficulties in 3D-HEVC is time computational complexity. The depth map intra-prediction is a critical factor in 3D-HEVC intra-coding, in which, the 3D-HEVC uses a highly adaptable Coding Unit (CU) structure with a specific end goal to expand the coding efficiency of all depth map characteristics. However, it results in an enormous Rate Distortion Optimization Cost (RDO-Cost) because of the broad recursive search for the best CU size from \(64\times 64\) down to \(4\times 4\). This computational complexity excludes the 3D-HEVC from true and real-time application. Hence, it is imperative to build up an algorithm to diminish the complexity of the size decision in depth map intra-coding. To determine the previously mentioned issue, this paper proposes an effective 3D-HEVC PU size decision algorithm for depth map intra-video coding based on tensor features and statistical data analyses. The experimental results demonstrate that the proposed model diminishes the complexity of depth map size decision significantly with low rate distortion increase.

Keywords

3D-HEVC JCT-3V Statistical data Depth map intra-coding Intra-CU coding 

Notes

References

  1. 1.
    Wang, S.: Special issue on real-time 3D imaging and processing. J. Real Time Image Process. 7(1), 1–2 (2012).  https://doi.org/10.1007/s11554-012-0241-1 MathSciNetCrossRefGoogle Scholar
  2. 2.
    Tech, G., Chen, Y., Müller, 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
  3. 3.
    Müller, K., Schwarz, H., Marpe, D., Bartnik, C., Bosse, S., Brust, H., Hinz, T., Lakshman, H., Merkle, P., Rhee, F.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
  4. 4.
    Sullivan, G.J., Boyce, J.M., Chen, Y., Ohm, J., Segall, C.A., Vetro, A.: Standardized extensions of high efficiency video coding (HEVC). IEEE J. Select. Top. Signal Process. 7(6), 1001–1016 (2013)CrossRefGoogle Scholar
  5. 5.
    Sullivan, G.J., 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
  6. 6.
    Kauff, P., Atzpadin, N., Fehn, C., Müller, M., Schreer, O., Smolic, A., Tanger, R.: Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability. Signal Process. Image Commun. 22(2), 217–234 (2007). (special issue on three-dimensional video and television) CrossRefGoogle Scholar
  7. 7.
    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.  https://doi.org/10.1007/s11554-017-0692-5
  8. 8.
    Fehn, C.: Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. Vol. 5291, pp. 93–104 (2004)Google Scholar
  9. 9.
    Chi, G., Jin, X., Dai, Q.: A quad-tree and statistics based fast CU depth decision algorithm for 3D-HEVC, in IEEE International Conference on Multimedia and Expo Workshops (ICMEW), vol. 2014, pp. 1–5 (2014)Google Scholar
  10. 10.
    Sullivan, G.J., Bjontegaard, G., Luthra, A.: Overview of the H.264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol. 13(7), 560–576 (2003)CrossRefGoogle Scholar
  11. 11.
    Zhang, H., Chan, Y., Fu, C., Tsang, S., Siu, W.: Quadtree decision for depth intra coding in 3D-HEVC by good feature, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1481–1485 (2016)Google Scholar
  12. 12.
    Peng, K., Chiang, J., Lie, W.: Low complexity depth intra coding combining fast intra mode and fast CU size decision in 3D-HEVC, in IEEE International Conference on Image Processing (ICIP), vol. 2016, pp. 1126–1130 (2016)Google Scholar
  13. 13.
    Chen, J., Wang, B., Zeng, H., Cai, C., Ma, K.-K.: Sum-of-gradient based fast intra coding in 3D-HEVC for depth map sequence (SOG-FDIC). J. Visual Commun. Image Represent. 48, 329–339 (2017)CrossRefGoogle Scholar
  14. 14.
    Kim, M., Ling, N., Song, L.: Fast single depth intra mode decision for depth map coding in 3D-HEVC, in IEEE International Conference on Multimedia Expo Workshops (ICMEW), vol. 2015, pp. 1–6 (2015)Google Scholar
  15. 15.
    Chiang, J.-C., Peng, K.-K., Wu, C.-C., Deng, C.-Y., Lie, W.-N.: Fast intra mode decision and fast CU size decision for depth video coding in 3D-HEVC. Signal Process. Image Commun. 71, 13–23 (2019).  https://doi.org/10.1016/j.image.2018.10.009 CrossRefGoogle Scholar
  16. 16.
    He, G., Hu, J., Li, Y., Yu, W., Yang, Z., Liu, P., Guo, R.: Fast mode decision and PU size decision algorithm for intra depth coding in 3D-HEVC. J. Visual Commun. Image Represent. 49, 303–314 (2017).  https://doi.org/10.1016/j.jvcir.2017.09.018 CrossRefGoogle Scholar
  17. 17.
    Hamout, H., Elyousfi, A.: Fast texture intra size coding based on big data clustering for 3D-HEVC, in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1728–1732 (2018).  https://doi.org/10.1109/ICASSP.2018.8462143
  18. 18.
    Hamout, H., Elyousfi, A.: Fast depth map intra coding for 3D video compression based tensor feature extraction and data analysis. IEEE Trans. Circuits Sys. Video Tech. (2019).  https://doi.org/10.1109/TCSVT.2019.2918770 Google Scholar
  19. 19.
    Yang, M., Lai, C.: A robust automatic merging possibilistic clustering method. IEEE Trans. Fuzzy Syst. 19(1), 26–41 (2011).  https://doi.org/10.1109/TCSVT.2019.2918770 CrossRefGoogle Scholar
  20. 20.
    Karczewicz, M., Chen, P., Joshi, R.L., Wang, X., Chien, W., Panchal, R., Reznik, Y., Coban, M., Chong, I.S.: A hybrid video coder based on extended macroblock sizes, improved interpolation, and flexible motion representation. IEEE Trans. Circuits Syst. Video Technol. 20(12), 1698–1708 (2010)CrossRefGoogle Scholar
  21. 21.
    Wallendael, G.V., Leuven, S.V., Cock, J.D., Bruls, F., de Walle, R.V.: 3D video compression based on high efficiency video coding. IEEE Trans. Consum. Electron. 58(1), 137–145 (2012)CrossRefGoogle Scholar
  22. 22.
    Sze, V., Budagavi, M., Sullivan, G.J.: High Efficiency Video Coding (HEVC). Springer International Publishing, Berlin (2014).  https://doi.org/10.1007/978-3-319-06895-4
  23. 23.
    Öztekin, A., Erçelebi, E.: An early split and skip algorithm for fast intra cu selection in HEVC. J. Real Time Image Process. 12(2), 273–283 (2016).  https://doi.org/10.1007/s11554-015-0534-2 CrossRefGoogle Scholar
  24. 24.
    Lainema, J., Bossen, F., Han, W., Min, J., Ugur, K.: Intra coding of the HEVC standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1792–1801 (2012)CrossRefGoogle Scholar
  25. 25.
    Lin, Y.C., Lai, J.C.: Edge density early termination algorithm for HEVC coding tree block, in International Symposium on Computer, Consumer and Control, vol. 2014, pp. 39–42 (2014).  https://doi.org/10.1109/IS3C.2014.23
  26. 26.
    Sole, J., Joshi, R., Nguyen, N., Ji, T., Karczewicz, M., Clare, G., Henry, F., Duenas, A.: Transform coefficient coding in HEVC. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1765–1777 (2012).  https://doi.org/10.1109/TCSVT.2012.2223055 CrossRefGoogle Scholar
  27. 27.
    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).  https://doi.org/10.1109/TCSVT.2010.2092615 CrossRefGoogle Scholar
  28. 28.
    Shen, L., Zhang, Z., Liu, Z.: Effective CU size decision for HEVC intracoding. IEEE Trans. Image Process. 23(10), 4232–4241 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Tech, G., Wegner, K., Chen, Y., Yea, S.: 3D HEVC draft text 6. Joint Collaborative Team on 3D Video Coding Extension Development Document JCT3V-J1001, 10th Meeting, Strasbourg, FRGoogle Scholar
  30. 30.
    Hamout, H., Elyousfi, A.: An efficient edge detection algorithm for fast intra-coding for 3D video extension of HEVC. J. Real Time Image Process.  https://doi.org/10.1007/s11554-017-0718-z
  31. 31.
    Chen, Y., Tech, G., Wegner, K., Yea, S.: Test model 11 of 3DHEVC and MV-HEVC. Joint Collaborative Team on 3D Video Coding Extension Development Document JCT3V-K1003, 11th Meeting, Geneva, CHGoogle Scholar
  32. 32.
    Jaballah, S., Larabi, M.-C., Tahar, J.B.: Low complexity intra prediction mode decision for 3D-HEVC depth coding. Signal Process. Image Commun. 67, 34–47 (2018).  https://doi.org/10.1016/j.image.2018.05.007 CrossRefGoogle Scholar
  33. 33.
    Lucas, L.F.R., Wegner, K., Rodrigues, N.M.M., Pagliari, C.L., da Silva, E.A.B., de Faria, S.M.M.: Intra predictive depth map coding using flexible block partitioning. IEEE Trans. Image Process. 24(11), 4055–4068 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Chen, Y., Tech, G., Wegner, K., Yea, S.: Test model 10 of 3DHEVC AND MV-HEVC. Joint Collaborative Team on 3D Video Coding Extension Development Document JCT3V-J1003, 10th Meeting, Strasbourg, FRGoogle Scholar
  35. 35.
    Saponara, S.: Real-time and low-power processing of 3D direct/inverse discrete cosine transform for low-complexity video codec. J. Real Time Image Process. 7(1), 43–53 (2012).  https://doi.org/10.1007/s11554-010-0174-5 CrossRefGoogle Scholar
  36. 36.
    Piao, Y., Min, J., Chen, J.: Encoder improvement of unified intra prediction. JCT-VC Document JCTVC-C207Google Scholar
  37. 37.
    Sanchez, G., Marcon, C., Agostini, L.: Real-time scalable hardware architecture for 3D-HEVC bipartition modes. J. Real Time Image Process. 13(1), 71–83 (2017)CrossRefGoogle Scholar
  38. 38.
    Gu, Z., Zheng, J., Ling, N., Zhang, P.: Fast intra prediction mode selection for intra depth map coding. ISO/IEC JTC1/SC29/WG11, ViennaGoogle Scholar
  39. 39.
    Chen, M., Yang, Y., Zhang, Q., Zhao, X., Huang, X., Gan, Y.: Low complexity depth mode decision for HEVC-based 3D video coding. Optik Int. J. Light Electron Opt. 127(11), 4758–4767 (2016)CrossRefGoogle Scholar
  40. 40.
    Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973).  https://doi.org/10.1080/01969727308546046 MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)CrossRefGoogle Scholar
  42. 42.
    Barni, M., Cappellini, V., Mecocci, A.: Comments on “A possibilistic approach to clustering”. IEEE Trans. Fuzzy Syst. 4(3), 393–396 (1996)CrossRefGoogle Scholar
  43. 43.
    Baghaie, A., Yu, Z.: Structure tensor based image interpolation method. CoRR abs/1402.5564. arXiv:1402.5564
  44. 44.
    Faraklioti, M., Petrou, M.: The Use of Structure Tensors in the Analysis of Seismic Data. (Springer, Berlin, 2005), pp. 47–88.  https://doi.org/10.1007/3-540-26493-0_3
  45. 45.
    Hamout, H., Elyousfi, A.: Low complexity intra mode decision algorithm for 3D-HEVC, in 2017 25th European Signal Processing Conference (EUSIPCO), pp. 1475–1479 (2017).  https://doi.org/10.23919/EUSIPCO.2017.8081454
  46. 46.
    Müller, K., Vetro, A.: Common test conditions of 3DV core experiments, Joint Collaborative Team on 3D Video Coding Extension Development Document JCT3V-G1100, 7th Meeting, San Jos, USGoogle Scholar
  47. 47.
    Joint Collaborative Team on 3D video coding (JCT-3V) HTM 16.2 Reference Software: [online]. https://hevc.hhi.fraunhofer.de/trac/3d-hevc/browser/3DVCSoftware/tags/HTM-16.2 (2016)
  48. 48.
    Tanimoto, M., Fujii, T., Suzuki, K.: View synthesis algorithm in view synthesis reference software 2.0 (VSRS2.0). Tech. Rep., ISO/IEC JTC1/SC29/WG11 M16090, Lausanne, Switzerland (2008)Google Scholar
  49. 49.
    Bjntegaard, G.: Calculation of average PSNR differences between RD curves. In: 13th VCEG Meeting, Document VCEGM33, Austin (2001)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Computer Systems and Vision Laboratory, Faculty of SciencesIbn-Zohr University, BP 8106AgadirMorocco
  2. 2.Department of Computer Science, National Engineering School of Applied Sciences (ENSA)Ibn-Zohr UniversityAgadirMorocco

Personalised recommendations