Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1547–1567 | Cite as

Objective HDR image quality assessment

  • Chih-Yang Lin
  • Kai-Ren Jheng
  • Timothy K. ShihEmail author
Article
  • 109 Downloads

Abstract

Although there is a lot of literature about generating HDR images, very limited research has been conducted on the issue of HDR image quality based on people’s feeling and preferences. The goal of this paper is to identify an objective quality measurement of an HDR image. The key features of HDR images that affect people’s preferences are uncovered through a survey in which testers judge different sample images of different scenes. The experimental results show that the proposed quality measurement for HDR images generates scores consistent with the true feelings of observers.

Keywords

High dynamic range Subjective preferences HDR image quality assessment 

Notes

References

  1. 1.
    Debevec PE, Malik J (2008) “Recovering high dynamic range radiance maps from photographs.” In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 369–378Google Scholar
  2. 2.
    Fattal R, Lischinski D, Werman M (2002) Gradient domain high dynamic range compression. ACM Trans Graph 21(3):249–256CrossRefGoogle Scholar
  3. 3.
    Ghimire D, Lee J (2011) Nonlinear transfer function-based local approach for color image enhancement. IEEE Trans Consum Electron 57(2):858–865CrossRefGoogle Scholar
  4. 4.
    Hanhart P, Bernardo MV, Pereira M, Pinheiro AM, Ebrahimi T (2015) Benchmarking of objective quality metrics for HDR image quality assessment. EURASIP J Image Video Process 1:39CrossRefGoogle Scholar
  5. 5.
    Hu J, Gallo O, Pulli K, Sun X (2013) “HDR deghosting: How to deal with saturation?.” In: In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1163–1170Google Scholar
  6. 6.
    Hulusic V, Valenzise G, Provenzi E, Debattista K, Dufaux F (2016) “Perceived dynamic range of HDR images.” In: Proceedings of 2016 Eighth International Conference on Quality of Multimedia Experience, pp. 1–6Google Scholar
  7. 7.
    Jia S, Zhang Y, Agrafiotis D, Bull D (2017) Blind high dynamic range image quality assessment using deep learning. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 765–769Google Scholar
  8. 8.
    Krasula L, Narwaria M, Fliegel K, Le Callet P (2015) “Influence of HDR reference on observers preference in tone-mapped images evaluation.” In: Proceedings of Seventh International Conference on Quality of Multimedia Experience, pp. 1–6Google Scholar
  9. 9.
    Kumar VA, Gupta S, Chandra SS, Raman S, Channappayya SS (2017) No-reference quality assessment of tone mapped High Dynamic Range (HDR) images using transfer learning. In: Proceedings of 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–3Google Scholar
  10. 10.
    Kundu D, Ghadiyaram D, Bovik AC, Evans BL (2016) “No-reference image quality assessment for high dynamic range images.” In: Proceedings of 50th Asilomar Conference on Signals, Systems and Computers, pp. 1847–1852Google Scholar
  11. 11.
    Kundu D, Ghadiyaram D, Bovik AC, Evans BL (2017) No-reference quality assessment of tone-mapped HDR pictures. IEEE Trans Image Process 26(6):2957–2971MathSciNetCrossRefGoogle Scholar
  12. 12.
    Mantiuk R, Kim KJ, Rempel AG, Heidrich W (2011) HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans Graph 30(4):40CrossRefGoogle Scholar
  13. 13.
    Mertens T, Kautz J, Reeth FV (2009) Exposure fusion: a simple and practical alternative to high dynamic range photography. Comput Graphics Forum 28(1):161–171CrossRefGoogle Scholar
  14. 14.
    Narwaria M, Silva MPD, Callet PL (2015) HDR-VQM: an objective quality measure for high dynamic range video. Signal Process Image Commun 35(1):46–60CrossRefGoogle Scholar
  15. 15.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRefGoogle Scholar
  16. 16.
    “Photomatix (2018): https://www.hdrsoft.com/”
  17. 17.
    Singh G, Khosla A, Anwar MI (2016) “Spatial domain color image enhancement based on local processing.” In: Proceedings of 3rd International Conference on Signal Processing and Integrated Networks, pp. 265–269Google Scholar
  18. 18.
    Zerman E, Valenzise G, Dufaux F (2017) An extensive performance evaluation of full-reference HDR image quality metrics. Qual User Experience 2(1):5CrossRefGoogle Scholar
  19. 19.
    Zhang Y, Chandler DM (2013) No-reference image quality assessment based on log-derivative statistics of natural scenes. J Electron Imaging 22(4):043025–043025CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Chih-Yang Lin
    • 1
  • Kai-Ren Jheng
    • 2
  • Timothy K. Shih
    • 2
    Email author
  1. 1.Department of Communications EngineeringYuan-Ze UniversityTaoyuanTaiwan
  2. 2.Department of Computer Science & Information Engineering, National Central UniversityTaoyuanTaiwan

Personalised recommendations