Skip to main content
SpringerLink
Log in

Introducing a Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database

  • 3DR Express
  • Published:
3D Research

Abstract

High dynamic range (HDR) displays and cameras are paving their ways through the consumer market at a rapid growth rate. Thanks to TV and camera manufacturers, HDR systems are now becoming available commercially to end users. This is taking place only a few years after the blooming of 3D video technologies. MPEG/ITU are also actively working towards the standardization of these technologies. However, preliminary research efforts in these video technologies are hammered by the lack of sufficient experimental data. In this paper, we introduce a Stereoscopic 3D HDR database of videos that is made publicly available to the research community. We explain the procedure taken to capture, calibrate, and post-process the videos. In addition, we provide insights on potential use-cases, challenges, and research opportunities, implied by the combination of higher dynamic range of the HDR aspect, and depth impression of the 3D aspect.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. MPEG document repository. (2017). http://phenix.int-evry.fr/jct/index.php

  2. Sullivan, G. J., et al. (2012). Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on Circuits and Systems for Video Technology, 22(12), 1649–1668. doi:10.1109/TCSVT.2012.2221191.

    Article  Google Scholar 

  3. Ohm, J. R., et al. (2012). Comparison of the coding efficiency of video coding standards—including high efficiency video coding (HEVC). IEEE Transactions on Circuits and Systems for Video Technology, 22(12), 1669–1684. doi:10.1109/TCSVT.2012.2221192.

    Article  Google Scholar 

  4. Multimedia Signal Processing Group at EPFL. (2017). http://mmspg.epfl.ch/downloads

  5. Winkler, S. (2017). Image and video quality resources. http://stefan.winklerbros.net/resources.html

  6. IRCCyN lab at Institut de Recherche en Communications et Cybernétique de Nante. (2017). http://ivc.univ-nantes.fr/en/

  7. Smolic, A., et al. (2007). Coding algorithms for 3DTV—A survey. IEEE Transactions on Circuits and Systems for Video Technology, 17(11), 1606–1621.

    Article  MathSciNet  Google Scholar 

  8. Muller, K., et al. (2013). 3D high-efficiency video coding for multi-view video and depth data. IEEE Transactions on Image Processing, 22(9), 3366–3378. doi:10.1109/TIP.2013.2264820.

    Article  MathSciNet  Google Scholar 

  9. Sullivan, G. J., et al. (2013). Standardized extensions of high efficiency video coding (HEVC). IEEE Journal of Selected Topics in Signal Processing, 7(6), 1001–1016. doi:10.1109/JSTSP.2013.2283657.

    Article  Google Scholar 

  10. Hannuksela, M., et al. (2013). Multiview-video-plus-depth coding based on the advanced video coding standard. IEEE Transactions on Image Processing, 22(9), 3449–3458. doi:10.1109/TIP.2013.2269274.

    Article  Google Scholar 

  11. Jiang, L., He, J., Zhang, N., et al. (2010). An overview of 3D video representation and coding. 3D Research, 1, 43. doi:10.1007/3DRes.01(2010)6.

    Article  Google Scholar 

  12. Rusanovskyy, D., Hannuksela, M. M., & Su, W. (2013). Depth-based coding of MVD data for 3D video extension of H.264/AVC. 3D Research, 4, 6. doi:10.1007/3DRes.02(2013)6.

    Article  Google Scholar 

  13. Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2012). A human visual system based 3D video quality metric. In 2nd international conference on 3D imaging, IC3D, December 2012, Belgium.

  14. Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2015). An efficient human visual system based quality metric for 3D video. Springer Journal of Multimedia Tools and Applications, 75(8), 4187–4215. doi:10.1007/s11042-015-2466-z.

    Article  Google Scholar 

  15. Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2013). 3D video quality metric for 3D video compression. 11th IEEE IVMSP workshop: 3D Image/Video Technologies and Applications, June 2013, Seoul, Korea.

  16. Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2013). A study on the relationship between the depth map quality and the overall 3D video quality of experience. In International 3DTV conference: vision beyond depth, October 2013, Scotland, UK.

  17. Hewage, C., et al. (2009). Quality evaluation of color plus depth map-based stereoscopic video. IEEE Journal of Selected Topics in Signal Processing, 3(2), 304–318. doi:10.1109/JSTSP.2009.2014805.

    Article  Google Scholar 

  18. Shao, F., et al. (2013). Perceptual full-reference quality assessment of stereoscopic images by considering binocular visual characteristics. IEEE Transactions on Image Processing, 22(5), 1940–1953. doi:10.1109/TIP.2013.2240003.

    Article  MathSciNet  Google Scholar 

  19. Zhang, W., et al. (2016). Using saliency-weighted disparity statistics for objective visual comfort assessment of stereoscopic images. 3D Research, 7, 17. doi:10.1007/s13319-016-0079-6.

    Article  Google Scholar 

  20. Banitalebi-Dehkordi, A., Nasiopoulos, E., Pourazad, M. T., & Nasiopoulos, P. (2017). Benchmark three-dimensional eye-tracking dataset for visual saliency prediction on stereoscopic three-dimensional video. SPIE Journal of Electronic Imaging, 25(1), 013008. doi:10.1117/1.JEI.25.1.013008. http://ece.ubc.ca/~dehkordi/databases.html

  21. Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2016). A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D). Multimedia Tools and Applications. doi:10.1007/s11042-016-4155-y.

    Google Scholar 

  22. Chagnon-Forget, M., Rouhafzay, G., Cretu, A. M., et al. (2016). Enhanced visual-attention model for perceptually improved 3D object modeling in virtual environments. 3D Research, 7, 30. doi:10.1007/s13319-016-0106-7.

    Article  Google Scholar 

  23. Ferwerda, J. A. (2001). Elements of early vision for computer graphics. Computer Graphics and Applications, 21(5), 22–33.

    Article  Google Scholar 

  24. Salih, Y., et al. (2012). Tone mapping of HDR images: A review. In 4th international conference on intelligent and advanced systems (ICIAS), 2012.

  25. Azimi, M., Banitalebi-Dehkordi, A., Dong, Y., Pourazad, M. T., & Nasiopoulos, P. (2014). Evaluating the performance of existing full-reference quality metrics on high dynamic range (HDR) video content. In ICMSP 2014: XII international conference on multimedia signal processing, November 2014, Venice, Italy.

  26. Banitalebi-Dehkordi, A., Azimi, M., Pourazad, M. T., & Nasiopoulos, P. (2014). Compression of high dynamic range video using the HEVC and H. 264/AVC standards. In 2014 10th international conference on heterogeneous networking for quality, reliability, security and robustness (QShine), Rhodes Island, Greece, August 2014 (invited paper).

  27. Yu, Sh., et al. (2016). Adaptive PQ: Adaptive perceptual quantizer for HEVC main 10 profile-based HDR video coding. In 2016 visual communications and image processing (VCIP) (pp. 1–4). doi:10.1109/VCIP.2016.7805499

  28. Jung, Ch., et al. (2016). HEVC encoder optimization for HDR video coding based on perceptual block merging. Visual Communications and Image Processing (VCIP). doi:10.1109/VCIP.2016.7805536.

    Google Scholar 

  29. Bouzidi, I., et al. (2016). On the selection of residual formula for HDR video coding. In 2016 6th European workshop on visual information processing (EUVIP) (pp. 1–5). doi:10.1109/EUVIP.2016.7764590

  30. Banitalebi-Dehkordi, A., Azimi, M., Pourazad, M. T., & Nasiopoulos, P. (2016). Visual saliency aided high dynamic range (HDR) video quality metrics. In International conference on communications (ICC), 2016.

  31. Korshunov, P., et al. (2015). Subjective quality assessment database of HDR images compressed with JPEG XT. In 2015 seventh international workshop on quality of multimedia experience (QoMEX) (pp. 1–6). doi:10.1109/QoMEX.2015.7148119

  32. Mantel, C., et al. (2014). Comparing subjective and objective quality assessment of HDR images compressed with JPEG-XT. In 2014 IEEE 16th international workshop on multimedia signal processing (MMSP) (pp. 1–6). doi:10.1109/MMSP.2014.6958833

  33. Banitalebi-Dehkordi, A., Dong, Y., Pourazad, M. T., & Nasiopoulos, P. (2015). A learning based visual saliency fusion model for high dynamic range video (LBVS-HDR). In 23rd European signal processing conference (EUSIPCO), 2015.

  34. Vavilin, A., & Jo, K.-H. (2011). Fast HDR image generation from multi-exposed multiple-view LDR images. In 3rd European workshop on visual information processing (EUVIP), July 2011.

  35. Sun, N., Mansour, H., & Ward, R. (2010). HDR image construction from multi-exposed stereo LDR images. In 17th international conference on image processing (ICIP), September 2010.

  36. Rufenacht, D. (2011). Stereoscopic high dynamic range video. Master Thesis, EPFL, August 2011.

  37. Selmanovic, E., et al. (2014). Enabling stereoscopic high dynamic range video. Signal Processing: Image Communication, 29(2), 216–228 (Special Issue on Advances in High Dynamic Range Video Research).

  38. RED Scarlet-X Operation Guide. (2017). https://red.com

  39. Recommendation ITU P.910. (1999). Subjective video quality assessment methods for multimedia applications, ITU.

  40. Xu, D., Coria, L. E., & Nasiopoulos, P. (2012). Guidelines for an improved quality of experience in 3D TV and 3D mobile displays. Journal of the Society for Information Display, 20(7), 397–407. doi:10.1002/jsid.99.

    Article  Google Scholar 

  41. Recommendation ITU-R BT.709-5. (2002). Parameter values for the HDTV standards for production and international programme exchange.

  42. Joint Video Team (JVT) of ISO/IEC MPEG & ITU-T VCEG. (2005). New test sequences in the VIPER 10-bit HD data. JVTQ090, 2005.

  43. Joint Video Team (JVT) of ISO/IEC MPEG & ITU-T VCEG. (2007). Donation of tone mapped image sequences. JVT-Y072, October, 2007.

  44. Lowe, D. G. (1999) Object recognition from local scale invariant features. In Proceedings of the international conference on computer vision (Vol. 2, pp. 1150–1157).

  45. Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2015). The effect of frame rate on 3D video quality and bitrate. Springer Journal of 3D Research, 6(1), 5–34. doi:10.1007/s13319-014-0034-3.

    Article  Google Scholar 

  46. Wgner, K., & Stankiewicz, K. (2014). DERS software manual. ISO/IEC JTC1/SC29/WG11 MPEG2014/M34302, July 2014, Sapporo, Japan.

  47. Tanimoto, M., Fujii, T., & Suzuki, K. (2009). Video depth estimation reference software (DERS) with image segmentation and block matching. ISO/IEC JTC1/SC29/WG11 MPEG/M16092, 2009.

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. University of British Columbia (UBC), Vancouver, BC, Canada

    Amin Banitalebi-Dehkordi

Authors
  1. Amin Banitalebi-Dehkordi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amin Banitalebi-Dehkordi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banitalebi-Dehkordi, A. Introducing a Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database. 3D Res 8, 3 (2017). https://doi.org/10.1007/s13319-017-0115-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13319-017-0115-1

Keywords

Search

Navigation