Modeling the Quality of Videos Displayed With Local Dimming Backlight at Different Peak White and Ambient Light Levels

Claire Mantel, Jacob Søgaard, Søren Bech, Jari Korhonen, Jesper Melgaard Pedersen, Søren Forchhammer

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This paper investigates the impact of ambient light and peak white (maximum brightness of a display) on the perceived quality of videos displayed using local backlight dimming. Two subjective tests providing quality evaluations are presented and analyzed. The analyses of variance show significant interactions of the factors peak white and ambient light with the perceived quality. Therefore, we proceed to predict the subjective quality grades with objective measures. The rendering of the frames on liquid crystal displays with light emitting diodes backlight at various ambient light and peak white levels is computed using a model of the display. Widely used objective quality metrics are applied based on the rendering models of the videos to predict the subjective evaluations. As these predictions are not satisfying, three machine learning methods are applied: partial least square regression, elastic net, and support vector regression. The elastic net method obtains the best prediction accuracy with a spearman rank order correlation coefficient of 0.71, and two features are identified as having a major influence on the visual quality.
Original languageEnglish
JournalIEEE Transactions on Image Processing
Issue number8
Pages (from-to)3751-3761
Publication statusPublished - 2016


  • LCD
  • Quality assessment
  • local backlight dimming
  • ambient light
  • peak white
  • Computer Graphics and Computer-Aided Design
  • Software
  • Ambient light
  • Local backlight dimming
  • Peak white
  • Artificial intelligence
  • Forecasting
  • Learning systems
  • Least squares approximations
  • Light emitting diodes
  • Liquid crystal displays
  • Luminance
  • Rendering (computer graphics)
  • Backlight dimming
  • Local-dimming backlight
  • Partial least square regression
  • Support vector regression (SVR)
  • Three machine learning methods
  • Quality control


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