Back to articles
Articles
Volume: 29 | Article ID: art00028
Image
Subjective viewer preference model for automatic HDR down conversion
  DOI :  10.2352/ISSN.2470-1173.2017.12.IQSP-242  Published OnlineJanuary 2017
Abstract

Although the idea of tone mapping has a long history, there is no tone mapping operator fulfilling the requirements of (live) broadcasting completely. But in times of HDR standards [1] it is more important than ever to find a reliable automatic down conversion suitable for all kinds of scenes to get an integrated workflow for HDR and SDR and to let the majority of the viewers dealing with legacy displays benefit from HDR. Most of the tone mapping operators (TMOs) do not outperform a so called camera TMO (classic photographic s-shaped camera encoding) in comparison studies, which can be explained as a problem of goal. Modelling the human visual system (HVS) can be remarkable different from creating a pleasing image based on aesthetic wishes and artistic intends. The aim of the paper is to report on the results measuring the viewer preference at dynamic range compression and to set up a model which can be used to enhance existing TMOs. Therefore, probands had to do their own grading influencing brightness, contrast, saturation and homogenization under varying outer conditions. It can be shown that the most important aspect of HDR is the increased reproduction of the scene contrast range and not the increased brightness. By using an optimized gradation and a slight local tone mapping a close impression can also be displayed on SDR screens.

Subject Areas :
Views 22
Downloads 7
 articleview.views 22
 articleview.downloads 7
  Cite this article 

L. Lenzen, M. Christmann, "Subjective viewer preference model for automatic HDR down conversionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XIV,  2017,  pp 191 - 197,  https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-242

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2017
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology