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NLME: a nonlinear motion estimation-based compression method for animated mesh sequence

  • Mohammadali Hajizadeh
  • Hossein EbrahimnezhadEmail author
Original Article
  • 28 Downloads

Abstract

This paper proposes an efficient compression algorithm for animated three-dimensional meshes by introducing nonlinear transformations to model the motion field of deforming patches. First, a segmentation process is applied to separate the 3D model into different patches which have similar motion patterns through the sequence. Next, the motion of the resulting patches is accurately described by a nonlinear motion estimation model. The main idea is to exploit the temporal coherence of the geometry component by using a nonlinear predictor in order to get better approximation of vertex locations. Nonlinear motion transforms are computed at previous frame to match the subsequent ones. Moreover, an adaptive bit allocation algorithm is employed to determine the near-optimal number of bits for quantizing the prediction errors. The number of quantization bits for each segmented patch is determined by analyzing the geometry complexity of the patch and the statistical properties of the prediction errors. Finally, an extensive experimental study has been conducted to evaluate the coding efficiency of the proposed compression scheme. Simulation results demonstrate that the proposed method is very efficient in terms of rate-distortion performance, particularly for the animated models with non-rigid deformations, and outperforms the state-of-the-art methods.

Keywords

Animated mesh sequence Dynamic mesh compression Rate-distortion Nonlinear motion modeling Motion segmentation 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Chen, J., Zheng, Y., Song, Y., Sun, H., Bao, H., Huang, J.: Cloth compression using local cylindrical coordinates. Vis. Comput. 33(6–8), 801–810 (2017)CrossRefGoogle Scholar
  2. 2.
    Lengyel, J. E.: Compression of time-dependent geometry. In: Proceedings of the 1999 Symposium on Interactive 3D Graphics, pp. 89–95. Atlanta (1999)Google Scholar
  3. 3.
    Mamou, K., Zaharia, T., Preteux, F.: A DCT-based approach for dynamic 3D mesh compression. WSEAS Transactions on information science and applications, pp. 1947–1954 (2006)Google Scholar
  4. 4.
    Ahn, J., Kim, C., Kuo, C., Ho, Y.: Motion-compensated compression of 3D animation models. Electron. Lett. 37(24), 1445–1446 (2001)CrossRefGoogle Scholar
  5. 5.
    Zhang, J., Owen, C.: Octree-based animated geometry compression. In: Proceedings of the Data Compression Conference, pp. 508–517 (2004)Google Scholar
  6. 6.
    Zhang, J., Owen, C.: Octree-based animated geometry compression. Comput. Graph. 31(3), 463–479 (2007)CrossRefGoogle Scholar
  7. 7.
    Muller, K., Smolic, A., Kautzner, M., Eisert, P., Wiegand, T.: Predictive compression of dynamic 3D meshes. In: 2005 IEEE International Conference on Image Processing, pp. 621–624 (2005)Google Scholar
  8. 8.
    Muller, K., Smolic, A., Kautzner, M., Eisert, P., Wiegand, T.: Rate-distortionoptimized predictive compression of dynamic 3D mesh sequences. Sig. Process. Image Commun. 21(9), 812–828 (2006)CrossRefGoogle Scholar
  9. 9.
    Mamou, K., Zaharia, T., Preteux, F.: A skinning approach for dynamic 3D mesh compression. Comput. Animat. Virtual Worlds 17(3–4), 337–346 (2006)CrossRefGoogle Scholar
  10. 10.
    Mamou, K., Zaharia, T., Preteux, F.: Famc: the mpeg-4 standard for animated mesh compression. In: 15th IEEE International Conference on Image Processing, pp. 2676–2679 (2008)Google Scholar
  11. 11.
    Hachani, M., Zaid, A.O., Puech, W.: Segmentation-based compression scheme for 3D animated models. SIViP 10(6), 1065–1072 (2016)CrossRefGoogle Scholar
  12. 12.
    Alexa, M., Muller, W.: Representing animations by principal components. Comput. Graph. Forum 19(3), 411–418 (2000)CrossRefGoogle Scholar
  13. 13.
    Karni, Z., Gotsman, C.: Compression of soft-body animation sequences. Comput. Graph. 28(1), 25–34 (2004)CrossRefGoogle Scholar
  14. 14.
    Lee, P.F., Kao, C.K., Tseng, J.L., Jong, B.S., Lin, T.W.: 3D animation compression using affine transformation matrix and principal component analysis. IEICE Trans. Inf. Syst. 90(7), 1073–1084 (2007)CrossRefGoogle Scholar
  15. 15.
    Amjoun, R., Sondershaus, R., Straser, W.: Compression of complex animated meshes. Advances in Computer Graphics, pp. 606–613 (2006)Google Scholar
  16. 16.
    Amjoun, R., Straser, W.: Efficient compression of 3-D dynamic mesh sequences. J WSCG 15(1–3), 32–46 (2007)Google Scholar
  17. 17.
    Lalos, A.S., Vasilakis, A.A., Dimas, A., Moustakas, K.: Adaptive compression of animated meshes by exploiting orthogonal iterations. Vis. Comput. 33(6–8), 811–821 (2017)CrossRefGoogle Scholar
  18. 18.
    Yang, J., Kim, C., Lee, S.: Compression of 3-D triangle mesh sequences based on vertex-wise motion vector prediction. IEEE Trans. Circuits Syst. Video Technol. 12(12), 1178–1184 (2002)CrossRefGoogle Scholar
  19. 19.
    Ibarria, L., Rossignac, J.: Dynapack: space-time compression of the 3D animations of triangle meshes with fixed connectivity. In: Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 126–135 (2003)Google Scholar
  20. 20.
    Stefanoski, N., Ostermann, J.: Connectivity-guided predictive compression of dynamic 3D meshes. In: 2006 IEEE International Conference on Image Processing, pp. 2973–2976 (2006)Google Scholar
  21. 21.
    Stefanoski, N., Liu, X., Klie, P., Ostermann, J.: Scalable linear predictive coding of time-consistent 3D mesh sequences. 3DTV-Conference The True Vision—Capture, Transmission and Display of 3D Video, pp. 1–4 (2007)Google Scholar
  22. 22.
    Stefanoski, N., Ostermann, J.: SPC: fast and efficient scalable predictive coding of animated meshes. Comput. Graph. Forum 29, 101–116 (2010)CrossRefGoogle Scholar
  23. 23.
    Bici, M.O., Akar, G.B.: Improved prediction methods for scalable predictive animated mesh compression. J. Vis. Commun. Image Represent. 22(7), 577–589 (2011)CrossRefGoogle Scholar
  24. 24.
    Guskov, I., Khodakovsky, A.: Wavelet compression of parametrically coherent mesh sequences. In: SCA’04: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Eurographics Association, pp. 183–192. Aire-la-Ville (2004)Google Scholar
  25. 25.
    Payan, F., Antonini, M.: Wavelet-based compression of 3D mesh sequences. In: Proceedings of IEEE ACIDCA-ICMI (2005)Google Scholar
  26. 26.
    Payan, F., Antonini, M.: Temporal wavelet-based compression for 3D animated models. Comput. Graph. 31(1), 77–88 (2007)CrossRefGoogle Scholar
  27. 27.
    Briceno, H., Sander, P., McMillan, L., Gortler, S., Hoppe, H.: Geometry videos: a new representation for 3D animations. In: Proceedings of ACM Symposium on Computer Animation, pp. 136–146 (2003)Google Scholar
  28. 28.
    Wang, S., Kong, D., Xue, J., Zhu, W., Xu, M., Yin, B., Roth, H.: Connectivity-preserving geometry images. Vis. Comput. 31(9), 1163–1178 (2015)CrossRefGoogle Scholar
  29. 29.
    Mamou, K., Zaharia, T., Preteux, F.: Multi-chart geometry video: a compact representation for 3D animations. In: Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 711–718 (2006)Google Scholar
  30. 30.
    Vasa, L., Skala, V.: Coddyac: connectivity driven dynamic mesh compression. In: 3DTV-Conference The True Vision—Capture, Transmission and Display of 3D Video, pp. 1–4 (2007)Google Scholar
  31. 31.
    Vasa, L., Marras, S., Hormann, K., Brunnett, G.: Compressing dynamic meshes with geometric Laplacians. Comput. Graph. Forum 33(2), 145–154 (2014)CrossRefGoogle Scholar
  32. 32.
    Hajizadeh, M.A., Ebrahimnezhad, H.: Predictive compression of animated 3D models by optimized weighted blending of key-frames. Comput. Animat. Virtual Worlds 27(6), 556–576 (2016)CrossRefGoogle Scholar
  33. 33.
    Hajizadeh, M.A., Ebrahimnezhad, H.: Eigenspace compression: dynamic 3D mesh compression by restoring fine geometry to deformed coarse models. Multimed Tools Appl (2017).  https://doi.org/10.1007/s11042-017-5394-2 Google Scholar
  34. 34.
    Liu, W., Ribeiro, E.: A survey on image-based continuum-body motion estimation. Image Vis. Comput. 29(8), 509–523 (2011)CrossRefGoogle Scholar
  35. 35.
    Guo, S., Southern, R., Chang, J., Greer, D., Zhang, J.J.: Adaptive motion synthesis for virtual characters: a survey. Vis. Comput. 31(5), 497–512 (2015)CrossRefGoogle Scholar
  36. 36.
    Lee, H., Lavoue, G., Dupont, F.: Rate-distortion optimization for progressive compression of 3D mesh with color attributes. Vis. Comput. 28(2), 137–153 (2012)CrossRefGoogle Scholar
  37. 37.
    Marpe, D., Schwarz, H., Wiegand, T.: Context-based adaptive binary arithmetic coding in the H. 264/AVC video compression standard. IEEE Trans. Circuits Syst. Video Technol. 13(7), 620–636 (2013)CrossRefGoogle Scholar
  38. 38.
    Vlasic, D., Baran, I., Matusik, W., Popovi´c, J.: Articulated mesh animation from multi-view silhouettes. ACM Trans. Graph. 27(3), 1–9 (2008)CrossRefGoogle Scholar
  39. 39.
    Aspert, N., Santa-Cruz, D., Ebrahimi, T.: MESH: measuring errors between surfaces using the hausdorff distance. In: Proceeding of the IEEE International Conference in Multimedia and Expo (ICME), pp. 705–708 (2002)Google Scholar
  40. 40.
    Vasa, L., Skala, V.: A perception correlated comparison method for dynamic meshes. IEEE Trans. Vis. Comput. Graph. 17(2), 220–230 (2011)CrossRefGoogle Scholar
  41. 41.
    Touma, C., Gostman, C.: Triangle mesh compression. In: Proceeding of Graphics Interface, pp. 26–34 (1998)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Computer Vision Research Lab, Department of Electrical EngineeringSahand University of TechnologyTabrizIran

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