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

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.

Modeling the subjective quality of highly contrasted videos displayed on LCD with local backlight dimming.

Local backlight dimming is a technology aiming at both saving energy and improving visual quality on television sets. As the rendition of the image is specified locally, the numerical signal corresponding to the displayed image needs to be computed through a model of the display. This simulated signal can then be used as input to objective quality metrics. The focus of this paper is on determining which characteristics of locally backlit displays influence quality assessment. A subjective experiment assessing the quality of highly contrasted videos displayed with various local backlight-dimming algorithms is set up. Subjective results are then compared with both objective measures and objective quality metrics using different display models. The first analysis indicates that the most significant objective features are temporal variations, power consumption (probably representing leakage), and a contrast measure. The second analysis shows that modeling of leakage is necessary for objective quality assessment of sequences displayed with local backlight dimming.