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Stereoscopic image generation with depth image based rendering

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Abstract

In this paper, we proposed stereoscopic image generation methods of adjusting the depth value of edge pixels and improved hole filling procedures. For the conventional system, the smooth of depth map can reduce the incidence of image holes, but cause geometric distortions of the image depth. To solve the problems, the depth map is first expanded to refine the accuracy of image depth and the quality of images. Next, we derive a hardware-oriented method for 3D warping. Finally, appropriate blocks are searched to enhance the performance of image by improving hole-filling procedures. The experimental results demonstrate the proposed methods have great performance and practicability.

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References

  1. Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image inpainting. Proc 27th Ann Conf Comput Graph Interact Tech:417–424

  2. Bosc E, Koppel M, Pepion R, Pressigout M, Morin L, Ndjiki-Nya R et al (2011) Can 3D synthesized views be reliably assessed through usual subjective and objective evaluation protocols?. 18th IEEE Int Conf Image Process (ICIP): 2597–2600

  3. Chen H-C ()2010 Real-time stereoscopic image generation using depth image based rendering with virtual view point estimation. Master’s Thesis, Dept. of Electrical Engineering, National Cheng Kung University

  4. Chen H-J, Lo F-H, Jan F-C, Wu S-D (2010) Real-time multiview rendering architecture for autostereoscopic displays. Proc IEEE Int Symp Circ Syst (ISCAS): 1165–1168

  5. Criminisi A, Pérez P, Toyama K (2004) Region filling and object removal by exemplar-based image inpainting. IEEE Trans Image Process 13:1200–1212

    Article  Google Scholar 

  6. Gortler SJ, Grzeszczuk R, Szeliski R, Cohen MF (1996) The lumigraph. Proc 23rd Ann Conf Comput Graph Interact Techn: 43–54

  7. Jung KH, Park YK, Kim JK, Lee H, Yun K, Hur N et al. (2008) Depth image based rendering for 3D data service over T-DMB. 3DTV Conf: True Vision-Capture, Trans Display 3D Video: 237–240

  8. Kubota A, Smolic A, Magnor M, Tanimoto M, Chen T, Zhang C (2007) Multiview imaging and 3DTV. IEEE Signal Process Mag 24(6):10–21

    Article  Google Scholar 

  9. Mark WR (1999) Post-rendering 3D image warping: visibility, reconstruction, and performance for depth image warping. PhD thesis, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

  10. McMillan L (1997) An image-based approach to three-dimensional computer graphics. PhD thesis, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

  11. Mori Y, Fukushima N, Yendo T, Fujii T, Tanimoto M (2009) View generation with 3D warping using depth information for FTV. Signal Process Image Commun 24(1–2):65–72

    Article  Google Scholar 

  12. Muller K, Smolic A, Dix K, Merkle P, Kauff P, Wiegand T (2008) View synthesis for advanced 3D video systems. EURASIP J Image Video Process: 1–11

  13. Ndjiki-Nya P, Koppel M, Doshkov D, Lakshman H, Merkle P, Muller K, Wiegand T (2011) “Depth image-based rendering with advanced texture synthesis for 3D video. IEEE Trans Multimed 13(3):453–465

    Article  Google Scholar 

  14. Oliveira MM (2000) Relief texture mapping. PhD thesis, University of North Carolina

  15. Sharma M, Chaudhury S, Lall B, Venkatesh MS (2014) A flexible architecture for multi-view 3DTV based on uncalibrated cameras. J Vis Commun Image Represent 25(4):599–621

    Article  Google Scholar 

  16. Shum H (2000) Review of image-based rendering techniques. Proc SPIE: 2–13

  17. Shum H, He L (1999) Rendering with concentric mosaics. Proc 26th Ann Conf Comput Graph Interact Techn: 299–306

  18. Smolic A (2009) An overview of 3D video and free viewpoint video. Sign Process:1–8

  19. Smolic A, Mller K, Dix K, Merkle P, Kauff P, Wiegand T (2008) Intermediate view interpolation based on multiview video plus depth for advanced 3D video systems. Image Process: 2448–2451

  20. Solh M, AlRegib G (2012) Hierarchical hole-filling for depth-based view synthesis in FTV and 3D video. IEEE J Select Topics Sign Process 6:495–504

    Article  Google Scholar 

  21. Sun S-P, Chou Y-J, Chiu Y-H (2010) Multimedia 3D clinical planning system for simulation of internal fixation surgery for calcaneal collapse. Multimed Tools Appl 52(1):5–18

    Article  Google Scholar 

  22. Szeliski R, Anandan P (2000) The geometry-image representation trade-off for rendering. Proc 2000 Int Conf Image Process 2:13–16

    Google Scholar 

  23. Tam WJ, Alain G, Zhang L, Martin T, Renaud R (2004) “Smoothing depth maps for improved stereoscopic image quality. Proc SPIE Conf Three-Dimensional TV, Video, Display III 5599:162–172

    Article  Google Scholar 

  24. Wang L, Hou C, Lei J, Yan W (2015) View generation with DIBR for 3D display system. Multimed Tools Appl 74(21):9529–9545

    Article  Google Scholar 

  25. Wang D, Zhao Y, Zheng W, Chen H (2015) Hole-filling for DIBR based on depth and gradient information. Int J Adv Robot Syst. doi:10.5772/60060

    Google Scholar 

  26. Xu X, Po L-M, Cheung K-W, Ng K-H, Wong K-M, Ting C-W (2012) A foreground biased depth map refinement method for DIBR view Synthesis. IEEE Int Conf Acoustics, Speech Signal Process (ICASSP): 805–808

  27. Xu Z, Sun J (2010) Image inpainting by patch propagation using patch sparsity. IEEE Trans Image Process 19:1153–1165

    Article  MathSciNet  Google Scholar 

  28. Yang SB, Liang TW (2012) Image restoration based on smooth gray-level detection and line prediction method for large missing regions. Int J Image Graph 12:1250013 [20 pages]

    Article  MathSciNet  Google Scholar 

  29. Zhang L, Tam WJ (2005) Stereoscopic image generation based on depth images for 3D TV. IEEE Trans Broadcast 51(2):1191–1199

    Article  Google Scholar 

  30. Zinger S, Ruijters D, de With PHN (2009) iGLANCE project: free-viewpoint 3D video. 17th Int Conf Comput Graph, Visual Comput Vision (WSCG):313–319

  31. Zitnick CL, Kang SB, Uyttendaele M, Winder S, Szeliski R (2004) High-quality video view interpolation using a layered representation. ACM Trans Graph (TOG) 23:600–608

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the National Science Council, R.O.C., under Grant MOST 104-2221-E-024-001

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Correspondence to Chi-Chou Kao.

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Kao, CC. Stereoscopic image generation with depth image based rendering. Multimed Tools Appl 76, 12981–12999 (2017). https://doi.org/10.1007/s11042-016-3733-3

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