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CART-based fast CU size decision and mode decision algorithm for 3D-HEVC

  • Ruihai Jing
  • Qian Zhang
  • Bin Wang
  • PengTao Cui
  • Tao Yan
  • Jifeng Huang
Original Paper
  • 43 Downloads

Abstract

Despite three-dimensional high efficiency video coding (3D-HEVC) has a good performance of 3D video coding and synthesized views, the recursive splitting process of the largest coding unit (LCU) and the best mode deciding process caused huge computational complexity. To reduce this computational burden, this paper presents a classification and regression tree-based (CART) fast coding level decision and mode decision algorithm for 3D depth video. The algorithm contained two parts: CART model training, fast coding level and mode decision. In the part of CART model training, we constructed a CART decision tree, where the complexity of depth map, the optimal depth level of co-located texture and the relativity of neighbor LCU were regarded as feature vectors, and the best coding unit depth level was regarded as class label. In the part of fast coding process, features were extracted to predict the depth level of each LCU of depth map; furthermore, some decision of coding mode could be skipped early. Experimental results show that proposed algorithm can save the times of coding process by 24.6% on average while maintaining almost the same rate distortion performance as the 3D-HEVC reference software.

Keywords

3D-HEVC Depth map Coding level classification 

Notes

Acknowledgements

This research was sponsored by Natural Science Foundation of China (Grant Nos. 61503251, 61741111).

References

  1. 1.
    Hong, S., Yang, D., Park, B., et al.: An efficient intra-mode decision method for HEVC. SIViP 10(6), 1–9 (2016)CrossRefGoogle Scholar
  2. 2.
    Ramezanpour, M., Zargari, F.: Fast CU size and prediction mode decision method for HEVC encoder based on spatial features. SIViP 10(7), 1233–1240 (2016)CrossRefGoogle Scholar
  3. 3.
    Xu, Y., Li, Q., Chen, J., et al.: Adaptive search range control in H.265/HEVC with error propagation resilience and hierarchical adjustment. Signal Image Video Process 11, 1–8 (2017)CrossRefGoogle Scholar
  4. 4.
    Song, Y., Ho, Y.S.: Unified depth intra coding for 3D video extension of HEVC. SIViP 8(6), 1031–1037 (2014)CrossRefGoogle Scholar
  5. 5.
    Lei, J., Sun, Z., Gu, Z., Zhu, T., Ling, N., Wu, F.: Simplified search algorithm for explicit wedgelet signalization mode in 3D-HEVC. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, pp. 805–810 (2017)Google Scholar
  6. 6.
    Zhao, J., Zhao, X., Zhang, W., et al.: An efficient depth modeling mode decision algorithm for 3D-HEVC depth map coding. Optik Int. J. Light Electron Opt. 127(24), 12048–12055 (2016)CrossRefGoogle Scholar
  7. 7.
    Liu, P., He, G., Xue, S., Li, Y.: A fast mode selection for depth modelling modes of intra depth coding in 3D-HEVC. In: 2016 Visual Communications and Image Processing (VCIP), Chengdu, pp. 1–4 (2016)Google Scholar
  8. 8.
    Guo, R., He, G., Li, Y., Wang, K.: Fast algorithm for prediction unit and mode decisions of intra depth coding in 3D-HEVC. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 1121–1125 (2016)Google Scholar
  9. 9.
    Park, C.S.: Edge-based intra mode selection for depth-map coding in 3D-HEVC. IEEE Trans. Image Process. 24(1), 155–162 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Jaballah, S., Larabi, M.C., Tahar, J.B.: Heuristic inspired search method for fast wedgelet pattern decision in 3D-HEVC. In: 2016 6th European Workshop on Visual Information Processing (EUVIP), Marseille, pp. 1–6 (2016)Google Scholar
  11. 11.
    Zhang, H.B., Chan, Y.L., Fu, C.H., et al.: Quadtree decision for depth intra coding in 3D-HEVC by good feature. In: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE (2016)Google Scholar
  12. 12.
    Gu, Z., Zheng, J., Ling, N., et al.: Fast depth modeling mode selection for 3D HEVC depth intra coding. IEEE Int. Conf. Multimed. Expo Workshops 2013, 1–4 (2013)Google Scholar
  13. 13.
    Zhang, Q., Zhang, N., Wei, T., et al.: Fast depth map mode decision based on depth texture correlation and edge classification for 3D-HEVC. J. Vis. Commun. Image Represent. 45(C), 170–180 (2017)CrossRefGoogle Scholar
  14. 14.
    Avila, G., Conceicao, R., Bubolz, T., Zatt, B., Porto, M., & Agostini, L., et al.: Complexity reduction of 3D-HEVC based on depth analysis for background and ROI classification. European Signal Processing Conference, pp. 1031–1035 (2017)Google Scholar
  15. 15.
    Zhang, Q., Wang, X., Huang, X., Su, R., Gan, Y.: Fast mode decision algorithm for 3D-HEVC encoding optimization based on depth information. Digit. Signal Process. 44, 37–46 (2015)CrossRefGoogle Scholar
  16. 16.
    Li, Y., Liu, X., Yu, T., Mei, Y., Wang, P.: Fast online-learning parameters decision algorithm based on bayesian decision rule for 3D-HEVC. In: IEEE, pp. 894–898 (2016)Google Scholar
  17. 17.
    Feng, X., Liu, P., Jia, K.: Fast intra mode decision algorithm for 3D-HEVC transcoding. In: Pan, J.S., Tsai, P.W., Watada, J., Jain, L. (eds.) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol. 82. Springer, Cham (2018)Google Scholar
  18. 18.
    da Silva, T.L., Agostini, L.V., da Silva Cruz, L.A.: Fast mode selection algorithm based on texture analysis for 3D-HEVC intra prediction. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), Turin, pp. 1–6 (2015)Google Scholar
  19. 19.
    Ren, H., Bai, H., Lin, C., Zhang, M., Zhao, Y.: Just noticeable difference based fast coding unit partition in 3D-HEVC intra coding. In: 2016 Data Compression Conference (DCC), Snowbird, UT, 2016, pp. 629–629Google Scholar
  20. 20.
    Zhang, H.B., Fu, C.H., Su, W.M., Tsang, S.H., Chan, Y.L.: Adaptive fast intra mode decision of depth map coding by low complexity RD-cost in 3D-HEVC. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), Singapore, pp. 487–491 (2015)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information, Mechanical and Electrical EngineeringShanghai Normal UniversityShanghaiChina
  2. 2.School of Information Science and TechnologyJiujiang UniversityJiujiangChina

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