Abstract
In a depth-image-based rendering (DIBR)-based 3D video system, the original 3D video is commonly compressed from the point of the video itself, paying little attention to its contribution to the synthesized virtual view. This paper first proposes a method to divide the original video into different regions according to its contribution to the synthesized view and then proposes to measure the regions using compressive sensing with different measurement rates, leading to a synthesis-aware region-based 3D video coding approach. Experimental results show that our approach can achieve better synthesized quality under the same equivalent measurement rate. Our approach is suitable for the applications when the virtual view is more important than the original views.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tian, Dong, Lai, Po-Lin, Lopez, Patrick, Gomila, Cristina: View synthesis techniques for 3D video. Proc. SPIE Appl. Digital Image Process. XXXII 7443, 74430T-1–74430-11 (2009)
Ndjiki-Nya, P., Köppel, M., Doshkov, D., Lakshman, H.: Depth image-based rendering with advanced texture synthesis for 3D video. IEEE Trans. Multimedia 13(3), 453–465 (2011)
Nguyen, Q.H., Do, M.N., Patel, S.J.: Depth image-based rendering with low resolution depth. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 553–556, November 2009
Müller, K., Schwarz, H., Marpe, D., Bartnik, C.: 3D high-efficiency video coding for multi-view video and depth data. IEEE Trans. Image Process. 22(9), 3366–3378 (2013)
Balota, G., Saldanha, M., Sanchez, G., Zatt, B., Porto, M., Agostini, L.: Overview and quality analysis in 3D-HEVC emergent video coding standard. In: 2014 IEEE 5th Latin American Symposium on Circuits and Systems (LASCAS), pp. 1–4 (2014)
Lee, C., Ho, Y-.S.: A framework of 3D video coding using view synthesis prediction. Picture Coding Symposium (PCS), pp. 9–12 (2012)
Duarte, M.F., Eldar, Y.C.: Structured compressed sensing: from theory to applications. IEEE Trans. Signal Process. 59(9), 4053–4085 (2011)
Do, T.T., Gan, L., Nguyen, N., Tran, T.D.: Sparsity adaptive matching pursuit algorithm for practical compressed sensing. In: Proceedings of the 42th Asilomar Conference on Signals, Systems, and Computers, pp. 581–587, Pacific Grove, California, October 2008
Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: Proceedings of the International Conference on Image Processing, pp. 3021–3024 Cairo, Egypt, November 2009
Gan, L.: Block compressed sensing of natural images. In: Proceedings of the International Conference on Digital Signal Processing, pp. 403–406, Cardiff, UK, July 2007
Acknowledgements
This work has been supported in part by National Natural Science Foundation of China (No. 61272262 and No. 61210006), International Cooperative Program of Shanxi Province (No. 2015031003-2), The Program of “One hundred Talented People” of Shanxi Province, Research Project Supported by Shanxi Scholarship Council of China (2014-056) and Program for New Century Excellent Talent in Universities (NCET-12-1037).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xing, Z., Wang, A., Jin, J., Wu, Y. (2015). Synthesis-Aware Region-Based 3D Video Coding. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-24078-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24077-0
Online ISBN: 978-3-319-24078-7
eBook Packages: Computer ScienceComputer Science (R0)