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Novel Encoding Time Reduction Algorithms for the 3D-HEVC Inter-Frame Prediction

  • Gustavo Sanchez
  • Luciano Agostini
  • César Marcon
Chapter
  • 120 Downloads

Abstract

This chapter presents the developed algorithms for encoding time reduction of the depth map inter-frame prediction. Three algorithms are presented along with their motivational analysis, where each algorithm actuate in a different encoding level. The first algorithm considered the simplicity of the depth maps and target Motion Estimation (ME) and Disparity Estimation (DE). The second proposal focuses on reusing the texture-encoded information for selecting a lower quantity of tools for being evaluated if these tools can obtain sound Rate-Distortion (RD) results. The last algorithm uses decision trees built with machine learning for pruning the quadtree evaluation when lower levels of quadtree are not required.

The experimental results regarding all algorithms are also presented in this chapter, and these results are compared to related works presented in Sect.  3.2. The algorithms presented in this chapter surpass other related works, presenting better results regarding encoding efficiency and timesaving.

Keywords

3D-HEVC Depth maps coding Inter-frame prediction Encoding time reduction Machine learning Statistical analysis Motion estimation Block-level algorithm Data mining Performance-aware algorithms 

References

  1. 1.
    Sanchez, G., M. Saldanha, B. Zatt, M. Porto, L. Agostini, and C. Marcon. 2017. Edge-aware depth motion estimation—A complexity reduction scheme for 3D-HEVC. In: European Signal Processing Conference, 1524–1528.Google Scholar
  2. 2.
    Saldanha, M., G. Sanchez, C. Marcon, and L. Agostini. 2018. Block-level fast coding scheme for depth maps in three-dimensional high efficiency video coding. Journal of Electronic Imaging 27 (1): 010502.CrossRefGoogle Scholar
  3. 3.
    ———. 2019. Fast 3D-HEVC depth maps encoding using machine learning. IEEE Transactions on Circuits and Systems for Video Technology 1–1: 12.Google Scholar
  4. 4.
    Zhu, S., and K.-K. Ma. 2000. A new diamond search algorithm for fast block-matching motion estimation. IEEE Transactions on Image Processing 9–2: 287–290.Google Scholar
  5. 5.
    Saldanha, M., G. Sanchez, B. Zatt, M. Porto, and L. Agostini. 2015. Complexity reduction for the 3D-HEVC depth maps coding. IEEE International Symposium on Circuits and Systems 2015: 621–624.Google Scholar
  6. 6.
    ———. 2017. Energy-aware scheme for the 3D-HEVC depth maps prediction. Journal of Real-Time Image Processing 13 (1): 55–69.CrossRefGoogle Scholar
  7. 7.
    Sanchez, G., M. Saldanha, G. Balota, B. Zatt, M. Porto, and L. Agostini. 2014. Complexity reduction for 3D-HEVC depth maps intra-frame prediction using simplified edge detector algorithm. In: IEEE International Conference on Image Processing, 3209–3213.Google Scholar
  8. 8.
    Correa, G., P.A. Assuncao, L.V. Agostini, and L.A. da Silva Cruz. 2015. Fast HEVC encoding decisions using data mining. IEEE Transactions on Circuits and Systems for Video Technology 25 (4): 660–673.CrossRefGoogle Scholar
  9. 9.
    Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten. 2009. The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11 (1): 10–18.CrossRefGoogle Scholar
  10. 10.
    Tuan, J.-C., T.-S. Chang, and C.-W. Jen. 2002. On the data reuse and memory bandwidth analysis for full-search block-matching VLSI architecture. IEEE Transactions on Circuits and Systems for Video Technology 12 (1): 61–72.CrossRefGoogle Scholar
  11. 11.
    Afonso, V., A. Susin, M. Perleberg, R. Conceição, G. Corrêa, L. Agostini, B. Zatt, and M. Porto. 2018. Hardware-friendly unidirectional disparity-search algorithm for 3d-hevc. IEEE International Symposium on Circuits and Systems: 1–5.Google Scholar
  12. 12.
    Conceição, R., G. Avila, G. Corrêa, M. Porto, B. Zatt, and L. Agostini. 2016. Complexity reduction for 3D-HEVC depth map coding based on early skip and early DIS scheme. IEEE International Conference on Image Processing 2016: 1116–1120.Google Scholar
  13. 13.
    Lei, J., J. Duan, F. Wu, N. Ling, and C. Hou. 2018. Fast mode decision based on grayscale similarity and inter-view correlation for depth map coding in 3D-HEVC. IEEE Transactions on Circuits and Systems for Video Technology 28 (3): 706–718.CrossRefGoogle Scholar
  14. 14.
    Mora, E.G., J. Jung, M. Cagnazzo, and B. Pesquet-Popescu. 2014. Initialization, limitation, and predictive coding of the depth and texture quadtree in 3D-HEVC. IEEE Transactions on Circuits and Systems for Video Technology 24 (9): 1554–1565.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gustavo Sanchez
    • 1
  • Luciano Agostini
    • 2
  • César Marcon
    • 3
  1. 1.Centro de InformáticaInstituto Federal Farroupilha (IFFAR)AlegreteBrazil
  2. 2.Video Technology Research Group (ViTech)Federal University of Pelotas (UFPel)PelotasBrazil
  3. 3.Polytechnic SchoolPontifical Catholic University of Rio Grande do Sul (PUCRS)Porto Alegre Brazil

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