Skip to main content
SpringerLink
Log in

An experimental study on the perceived quality of natively graded versus inverse tone mapped high dynamic range video content on television

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

High Dynamic Range (HDR) television promises to display higher brightness and deeper black levels and thus more vivid and realistic images. However, home video distribution and video broadcasting were historically designed for what we now call standard dynamic range screens (SDR). In order to display SDR content on an HDR screen, it is explicitly or implicitly converted, in a process called inverse tone mapping (iTMO). This paper’s goal is to assess the perceived quality of converted SDR content in comparison to natively graded HDR content. In doing so, this paper aims to enable content creators/distributors to make informed choices between creating/broadcasting HDR content or relying on conversion. To this end, a psychophysical experiment was performed to tests how viewers evaluate the difference between natively graded HDR and a set of SDR to HDR conversion options in a television setup. Results indicate that viewers prefer natively graded HDR content, followed by inverse tone mapping algorithms starting from videos with a compressed dynamic range. When comparing conversion options, users clearly prefer conversion from ‘compressed dynamic range’ SDR over ‘clipped dynamic range’ SDR. Users disliked videos that were naively stretched from standard SDR. In addition, a significant effect of type of sequence was found, with a preference for light scenes with low contrast.

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

Similar content being viewed by others

References

  1. Abebe M A, Pouli T, Kervec J (2015) Evaluating the color fidelity of itmos and hdr color appearance models. ACM Trans Appl Percept (TAP) 12 (4):14

    Google Scholar 

  2. Aydin T O, Mantiuk R, Myszkowski K, Seidel H P (2008) Dynamic range independent image quality assessment. ACM Trans Graph (TOG) 27(3):69

    Article  Google Scholar 

  3. Banterle F, Ledda P, Debattista K, Bloj M, Artusi A, Chalmers A (2009) A psychophysical evaluation of inverse tone mapping techniques. Comput Graph Forum Wiley Online Library 28(1):13–25

    Article  Google Scholar 

  4. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48

    Article  Google Scholar 

  5. Bist C, Cozot R, Madec G, Ducloux X (2017) Tone expansion using lighting style aesthetics. Comput Graph 62:77–86

    Article  Google Scholar 

  6. Chalmers A, Karr B, Suma R, Debattista K (2016) Fifty shades of hdr. In: Digital media industry & academic forum (DMIAF). IEEE, pp 53–58

  7. Chen C R, Chiu C T, Chang Y C (2011) Inverse tone mapping operator evaluation using blind image quality assessment. In: Asia–Pacific Sign and Information Proceedings of Association Annual Summit and Conf. APSIPA

  8. Daly S J, Feng X (2003) Bit-depth extension using spatiotemporal microdither based on models of the equivalent input noise of the visual system. In: Color imaging VIII: processing, Hardcopy, and Applications, International Society for Optics and Photonics, vol 5008, pp 455–467

  9. Daly S J, Feng X (2004) Decontouring: Prevention and removal of false contour artifacts. In: Human vision and electronic imaging IX, International Society for Optics and Photonics, vol 5292, pp 130– 150

  10. Daly S, Kunkel T, Sun X, Farrell S, Crum P (2013a) 41.1: Distinguished paper: Viewer preferences for shadow, diffuse, specular, and emissive luminance limits of high dynamic range displays. SID Symp Digest Techn Papers Wiley Online Library 44(1):563–566

  11. Daly S, Kunkel T, Sun X, Farrell S, Crum P (2013b) Preference limits of the visual dynamic range for ultra high quality and aesthetic conveyance. In: Human Vision and Electronic Imaging XVIII. Int Soc Opt Photon8651:86510J

  12. De Paepe AL, Crombez G, Legrain V (2017) Remapping nociceptive stimuli into a peripersonal reference frame is spatially locked to the stimulated limb. Neuropsychologia 101:121–131

  13. Divakaran A (2001) An overview of mpeg-7 motion descriptors and their applications. In: International Conference on Computer Analysis of Images and Patterns. Springer, pp 29–40

  14. Dong Y, Pourazad M T, Nasiopoulos P (2016) Human visual system-based saliency detection for high dynamic range content. IEEE Trans Multimed 18(4):549–562

    Article  Google Scholar 

  15. Durnez W, Van Damme S (2015) Trying to Fix a Painful Problem: The Impact of Pain Control Attempts on the Attentional Prioritization of a Threatened Body Location. J Pain 16(2):135–143

  16. Hanhart P, Korshunov P, Ebrahimi T, Thomas Y, Hoffmann H (2015) Subjective quality evaluation of high dynamic range video and display for future tv. SMPTE Motion Imaging J 124(4):1–6

    Article  Google Scholar 

  17. Holm S (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics:65–70

  18. ITU (2012) Methodology for the subjective assessment of the quality of television pictures BT Series Broadcasting service. Int Telecommun Union 13:1–48

  19. Jamieson S, et al. (2004) Likert scales: how to (ab) use them. Med Educ 38(12):1217–1218

    Article  Google Scholar 

  20. Jeannin S, Divakaran A (2001) Mpeg-7 visual motion descriptors. IEEE Trans Circ Syst Video Technol 11(6):720–724

    Article  Google Scholar 

  21. Khanh T, Bodrogi P, Vinh Q, Stojanovic D (2017) Colour preference, naturalness, vividness and colour quality metrics, part 1: Experiments in a room. Light Res Technol 49(6):697–713

    Article  Google Scholar 

  22. Kovaleski RP, Oliveira MM (2014) High-quality reverse tone mapping for a wide range of exposures. In: 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, pp 49–56

  23. Kuzon Jr W M, Urbanchek M G, McCabe S (1996) The seven deadly sins of statistical analysis. Ann Plast Surg 37(3):265–272

    Article  Google Scholar 

  24. Ledda P, Santos LP, Chalmers A (2004) A local model of eye adaptation for high dynamic range images. In: Proceedings of the 3rd international conference on Computer graphics, virtual reality, visualisation and interaction in Africa. ACM, pp 151–160

  25. Lee J S, De Simone F, Ebrahimi T (2011) Subjective quality evaluation via paired comparison: Application to scalable video coding. IEEE Trans Multimed 13(5):882–893

    Article  Google Scholar 

  26. Lee S, An GH, Kang SJ (2018) Deep chain hdri: Reconstructing a high dynamic range image from a single low dynamic range image. arXiv:180106277

  27. Luthra A, Franċois E, Husak W (2016) Requirements and use cases for hdr and wcg content distribution. ISO/IEC JTC 1/SC 29/WG 11 (MPEG) Doc N15084

  28. Luzardo G, Aelterman J, Luong H, Philips W, Ochoa D, Rousseaux S (2018) Fully-automatic inverse tone mapping preserving the content creator’s artistic intentions. In: Picture Coding Symposium 2018. IEEE, pp 53–58

  29. Mai Z, Mansour H, Nasiopoulos P, Ward R K (2013) Visually favorable tone-mapping with high compression performance in bit-depth scalable video coding. IEEE Trans Multimed 15(7):1503–1518

    Article  Google Scholar 

  30. Marnerides D, Bashford-Rogers T, Hatchett J, Debattista K (2018) Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content. arXiv:180302266

  31. Masia B, Serrano A, Gutierrez D (2017) Dynamic range expansion based on image statistics. Multimed Tools Appl 76(1):631–648

    Article  Google Scholar 

  32. Masia B, Agustin S, Fleming R W, Sorkine O, Gutierrez D (2009) Evaluation of reverse tone mapping through varying exposure conditions. ACM Trans Graph (TOG) 28(5):160

    Article  Google Scholar 

  33. Mukherjee R, Debattista K, Bashford-Rogers T, Waterfield B, Chalmers A (2016) A study on user preference of high dynamic range over low dynamic range video. Vis Comput 32(6-8):825–834

    Article  Google Scholar 

  34. Myszkowski K, Mantiuk R, Krawczyk G (2008) High dynamic range video. Synth Lect Comput Graph Animation 1(1):1–158

    Google Scholar 

  35. Norman G (2010) Likert scales, levels of measurement and the “laws” of statistics. Adv Health Sci Educ 15(5):625–632

    Article  Google Scholar 

  36. Pouli T, Pines J (2016) Hdr content creation: creative and technical challenges. In: ACM SIGGRAPH 2016 Courses. ACM, pp 14

  37. Reinhard E, Heidrich W, Debevec P, Pattanaik S, Ward G, Myszkowski K (2010) High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann

  38. SIM2 HDR display - HDR47ES6MB (2017). http://hdr.sim2.it/hdrproducts/hdr47es6mb

  39. Shokrollahi A, Mahmoudi-Aznaveh A, Maybodi B M N (2017) Image quality assessment for contrast enhancement evaluation. AEU-Int J Electron Commun 77:61–66

    Article  Google Scholar 

  40. Smith M, Zink M (2015) Managing hdr content production and display device capabilities, pp 11–11. https://doi.org/10.1049/ibc.2015.0031

  41. Verbruggen F, Aron AR (2010) Theta burst stimulation dissociates attention and action updating in human inferior frontal cortex. Proc Natl Acad Sci 107(31):13966–13971

  42. Wadgave U, Khairnar M R (2016) Parametric tests for likert scale: For and against. Asian J Psych 24:67–68

    Article  Google Scholar 

  43. Wang T H, Chiu C W, Wu W C, Wang J W, Lin C Y, Chiu C T, Liou J J (2015) Pseudo-multiple-exposure-based tone fusion with local region adjustment. IEEE Trans Multimed 17(4):470–484

    Article  Google Scholar 

  44. Ward G, Simmons M (2006) Jpeg-hdr: a backwards-compatible, high dynamic range extension to jpeg. In: ACM SIGGRAPH, vol 2006 Courses. ACM, pp 3

  45. West BT, Welch KB, Gałecki AT (2007) Linear Mixed Models: A Practical Guide Using Statistical Software. Chapman and Hall/CRC

  46. Winkler S (2005) Digital video quality: vision models and metrics. Wiley, New York

  47. You J, Korhonen J, Perkis A, Ebrahimi T (2011) Balancing attended and global stimuli in perceived video quality assessment. IEEE Trans Multimed 13(6):1269–1285

    Article  Google Scholar 

  48. Zerman E, Valenzise G, Dufaux F (2017) An extensive performance evaluation of full-reference hdr image quality metrics. Qual User Exper 2(1):5

    Article  Google Scholar 

  49. Zhang Y, Reinhard E, Bull D (2011) Perception-based high dynamic range video compression with optimal bit-depth transformation. In: 2011 18th IEEE International Conference on Image Processing (ICIP). IEEE, pp 1321–1324

Download references

Acknowledgements

This work was supported by the imec-ICON-HD2R project, co-funded by imec, a digital research institute founded by the Flemish Government. Project partners are Barco, Grass Valley, Limecraft, VRT, and Grid. The work of G. Luzardo was supported by Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) and Escuela Superior Politécnica del Litoral (ESPOL). Jan Aelterman is currently supported by a Ghent University postdoctoral fellowship (BOF15/PDO/003).

Author information

Authors and Affiliations

  1. imec-IPI-UGent, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Gonzalo Luzardo, Tine Vyvey, Jan Aelterman, Hiep Luong, Wouter Durnez, Jan Van Looy & Wilfried Philips

  2. Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Tom Paridaens, Glenn Van Wallendael & Peter Lambert

  3. Vlaamse Radio -en Televisieomroeporganisatie,, Auguste Reyerslaan 52, Brussels, Belgium

    Sven Rousseaux

  4. Facultad de Ingeniería en Electricidad y Computación, ESPOL Polytechnic University, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, Guayaquil, Ecuador

    Gonzalo Luzardo & Daniel Ochoa

Authors
  1. Gonzalo Luzardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tine Vyvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Aelterman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tom Paridaens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Glenn Van Wallendael
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peter Lambert
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sven Rousseaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hiep Luong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wouter Durnez
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jan Van Looy
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wilfried Philips
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Daniel Ochoa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gonzalo Luzardo.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luzardo, G., Vyvey, T., Aelterman, J. et al. An experimental study on the perceived quality of natively graded versus inverse tone mapped high dynamic range video content on television. Multimed Tools Appl 80, 5559–5576 (2021). https://doi.org/10.1007/s11042-020-09955-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09955-7

Keywords

Search

Navigation