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Current Research Results on Depth Map Interpolation Techniques

  • Stefania Colonnese
  • Stefano Rinauro
  • Gaetano Scarano
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 15)

Abstract

The goal of this chapter is twofold. Firstly, we provide the reader with a summary of the state-of-the-art depth map interpolation techniques. Secondly, we present recent results making use of markovian priors for depth map interpolation. Specifically, we provide insight into Markovian mathematical models recently presented in the literature, and we show that such priors allow interpolation without making use of contemporaneously acquired luminance views. This feature extends the scope of the interpolation procedure and decouple its performances from those of the luminance/depth images registration stage.

Keywords

Depth map Interpolation Markov random fields 

References

  1. 1.
    Ben Hadj S, Blanc-FTraud L, Maalouf E, Colicchio B, Dieterlen A (2012) Depth-variant image restoration in 3D fluorescence microscopy: two approaches under Gaussian and Poissonian noise conditions. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp 1671–1674, 2–5 May 2012Google Scholar
  2. 2.
    Chantas GK, Galatsanos NP, Likas AC (2006) Bayesian resroration using a new nonstationary edge-preserving image prior. IEEE Trans Image Process 15(10):2987–2997CrossRefMathSciNetGoogle Scholar
  3. 3.
    Chen CC, Chen YW, Yang FY, Peng WH (2009) A synthesis-quality-oriented depth refinement scheme for MPEG free viewpoint television (FTV). In: IEEE International Symposium on Multimedia, (ISM), pp 171–178, 14–16 Dec 2009Google Scholar
  4. 4.
    Colonnese S, Rinauro S, Scarano G (2011) Markov random fields using complex line process: an application to Bayesian image restoration. In: European Workshop on Visual Information Processing (EUVIP), 2–4 July 2011Google Scholar
  5. 5.
    Colonnese S, Randi R, Rinauro S, Scarano G (2010) Fast image interpolation using circular harmonic functions. In: European Workshop on Visual Information Processing (EUVIP), 5–7 July 2010Google Scholar
  6. 6.
    Colonnese S, Campisi P, Panci G, Scarano G (2004) Blind image deblurring driven by nonlinear processing in the edge domain. EURASIP J Appl Signal Process 16:2462–2475Google Scholar
  7. 7.
    Colonnese S, Rinauro S, Scarano G (2012) Bayesian image interpolation using Markov random fields driven by visually relevant image features. Signal Process: Image Commun 28(8):967–983Google Scholar
  8. 8.
    Colonnese S, Rinauro S, Scarano G (2012) Bayesian depth map interpolation using edge driven Markov Random fields. In: Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications (COMPIMAGE), 5–7 Sept 2012Google Scholar
  9. 9.
    Colonnese S, Rinauro S, Rossi L, Scarano G (2009) Visual relevance evaluation using Rate Distortion analysis in the circular harmonic functions domain. In: IEEE International Conference Image Processing (ICIP)Google Scholar
  10. 10.
    Deng H, Yu L, Qiu J, Zhang J (2012) A joint texture/depth edge-directed up-sampling algorithm for depth map coding. In: IEEE International Conference on Multimedia and Expo (ICME), 9–13 July 2012Google Scholar
  11. 11.
    De Silva DVSX, Ekmekcioglu E, Fernando WAC, Worrall ST (2011) Display dependent preprocessing of depth maps based on just noticeable depth difference modeling. IEEE J Sel Topics Signal Process 5(2):335–351CrossRefGoogle Scholar
  12. 12.
    Ekmekcioglu E, Mrak M, Worrall ST, Kondoz AM (2009) Edge adaptive upsampling of depth map videos for enhanced free-viewpoint video quality. IET Electron Lett 45(7):353–354CrossRefGoogle Scholar
  13. 13.
    Garro V, dal Mutto C, Zanuttigh P, Cortelazzo GM (2009) A novel interpolation scheme for range data with side information. In: European Conference Visual Media Production (CVMP) , 12–13 Nov 2009Google Scholar
  14. 14.
    Goffredo M, Schmid M, Conforto S, Carli M, Neri A, D”Alessio T (2009) Markerless human motion analysis in Gauss-Laguerre transform domain: an application to sit-to-stand in young and elderly people. IEEE Trans Inf Technol Biomed 13(2):207–216Google Scholar
  15. 15.
    Geman S, Geman D (1984) Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell PAMI-6(6):721–741Google Scholar
  16. 16.
    Graziosi DB, Dong T, Vetro A (2012) Depth map up-sampling based on edge layers. In: Asia-Pacific Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 1–4, 3–6 Dec 2012Google Scholar
  17. 17.
  18. 18.
    Jacovitti G, Neri A (2000) Multiresolution circular harmonic decomposition. IEEE Trans Signal Process 48(11):3242–3247CrossRefMathSciNetGoogle Scholar
  19. 19.
    Jaesik P, Hyeongwoo K, Tai YW, Brown MS, Kweon I (2011) High quality depth map upsampling for 3D-TOF cameras. In: IEEE International Conference on Computer Vision (ICCV), pp 1623–1630, 6–13 Nov 2011Google Scholar
  20. 20.
    JSger F, Wien M, Kosse P (2012) Model-based intra coding for depth maps in 3D video using a depth lookup table. In: 3DTV-Conference: The True Vision—Capture, Transmission and Display of 3D Video (3DTV-CON), pp 1–4, 15–17 Oct 2012Google Scholar
  21. 21.
    Lee EK, Ho YS (2011) Generation of high-quality depth maps using hybrid camera system for 3-D video. J Vis Commun Image Represent 22(1):73–84Google Scholar
  22. 22.
    Lee PJ (2011) Nongeometric distortion smoothing approach for depth map preprocessing. IEEE Trans Multimedia 13(2):246–254CrossRefGoogle Scholar
  23. 23.
    Lee PJ (2010) Adaptive edge-oriented depth image smoothing approach for depth image based rendering. In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp 1–5, 24–26 March 2010Google Scholar
  24. 24.
    Lee G, Ho Y (2011) Depth map up-sampling using random walk. In: Pacific Rim Symposiumv (PSIVT), 20–23 Nov 2011Google Scholar
  25. 25.
    Lu J, Min D, Singh Pahwa R, Do MN (2011) A revisit to MRF-based depth map super resolution and enhancement. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22–27 May 2011Google Scholar
  26. 26.
    Nguyen VA, Min D, Do MN (2012) Efficient edge-preserving interpolation and in-loop filters for depth map compression. IEEE International Conference on Image Processing (ICIP), pp 1293–1296 Sept 30–Oct. 3 2012Google Scholar
  27. 27.
    Palma V, Cancellaro M, Neri A (2011) Joint distributed source-channel coding for 3D videos. Image processing: algorithms and systems IX. In: Proceedings of the SPIE. doi:DOIurl10(1117/12):872878Google Scholar
  28. 28.
    Rong Z, Ying C, Karczewicz M (2012) Adaptive depth edge sharpening for 3D video depth coding. In: IEEE Visual Communications and Image Processing (VCIP), pp 1–6, 27–30 Nov 2012Google Scholar
  29. 29.
    Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1–3):7–42CrossRefMATHGoogle Scholar
  30. 30.
    Schwarz H, Bartnik C, Bosse S, Brust H, Hinz T, Lakshman H, Marpe D, Merkle P, Mnller K, Rhee H, Tech G, Winken M, Wiegand T (2012) 3D video coding using advanced prediction, depth modeling, and encoder control methods. In: Picture Coding Symposium (PCS), pp 1–4, 7–9 May 2012Google Scholar
  31. 31.
    Schwarz S, Olsson R, Sjöström M, Tourancheau S (2012) Adaptive depth filtering for HEVC 3D video coding. In: Picture Coding Symposium (PCS), pp 49–52, 7–9 May 2012Google Scholar
  32. 32.
    Smisek J, Jancosek M, Pajdla T (2011) 3D with Kinect. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp 1154–1160, 6–13 Nov 2011Google Scholar
  33. 33.
    Spatial Scalability Filters, ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6 (2005) Doc. JVT-P007, Ponzan, PolandGoogle Scholar
  34. 34.
    Timmerer C, Mnller K (2010) Immersive future media technologies: from 3D video to sensory experiences. ACM Multimedia (Tutorial), 25–29 Oct 2010Google Scholar
  35. 35.
    Tseng SP, Lai SH (2011) Accurate depth map estimation from video via MRF optimization. In: IEEE Visual Communications and Image Processing (VCIP), pp 1–4, 6–9 Nov 2011Google Scholar
  36. 36.
    Vijayanagar KR, Loghman M, Joohee K (2012) Refinement of depth maps generated by low-cost depth sensors. In: International SoC Design Conference (ISOCC), pp 355–358, 4–7 Nov 2012Google Scholar
  37. 37.
    Wang HM, Huang CH, Yang JF (2011) Block-based depth maps interpolation for efficient multiview content generation. IEEE Trans Circuits Syst Video Technol 21(12):1847–1858CrossRefGoogle Scholar
  38. 38.
    Wang HM, Huang CH, Yang JF (2010) Depth maps interpolation from existing pairs of keyframes and depth maps for 3D video generation. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp 3248–3251, May 30 - June 2 2010Google Scholar
  39. 39.
    Wildeboer MO, Yendo T, Tehrani MP, Fujii T, Tanimoto M (2010) Color based depth up-sampling for depth compression. In: Picture Coding Symposium (PCS), pp 170–173, 8–10 Dec 2010Google Scholar
  40. 40.
    Xu X, Po LM, Cheung, KW, Ng KH, Wong KM, Ting CW (2012) A foreground biased depth map refinement method for DIBR view synthesis. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 805–808, 25–30 March 2012Google Scholar
  41. 41.
    Zhang G, Jia J, Wong TT, Bao H (2009) Consistent depth maps recovery from a video sequence. IEEE Trans Pattern Anal Mach Intell 31(6):974–988Google Scholar
  42. 42.
    Zhu J (2011) Reliability fusion of time-of-flight depth and stereo geometry for high quality depth maps. IEEE Trans Pattern Anal Mach Intell 33(7):1400–1414Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Stefania Colonnese
    • 1
  • Stefano Rinauro
    • 1
  • Gaetano Scarano
    • 1
  1. 1.DIET, University of Rome “Sapienza”RomeItaly

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