High Dynamic Range (HDR) and Wide Color Gamut (WCG) displays require adapted color measurements analysis. In this paper, we evaluate the viewing angle dependence of the color gamut and color volume of two HDR/WCG displays, one QLED TV and one OLED TV measured using a Fourier optics viewing angle system. The analysis is made using L*a*b* color space and ICtCp color space recently proposed by Dolby laboratories. The different interests of the ICtCp color space for direct comparison of the displays is discussed.
Recent work in prediction of overall HDR and WCG display quality has shown that machine learning approaches based on physical measurements performs on par with more advanced perceptually transformed measurements. While combining machine learning with the perceptual transforms did improve over using each technique separately, the improvement was minor. However, that work did not explore how well these models performed when applied to display capabilities outside of the training data set. This new work examines what happens when the machinelearning approaches are used to predict quality outside of the training set, both in terms of extrapolation and interpolation. While doing so, we consider two models – one based on physical display characteristics, and a perceptual model that transforms physical parameters based on human visual system models. We found that the use of the perceptual transforms particularly helps with extrapolation, and without their tempering effects, the machine learning-based models can produce wildly unrealistic quality predictions.
The choice of primaries for a color display involves tradeoffs between different desirable attributes such as a large color gamut, high spectral reproduction accuracy, minimal observer metamerism, and low power consumption. Optimization of individual attributes often drives primary choices in different directions. For example, expansion of color gamut favors narrow spectral bandwidth saturated primaries and minimization of observer metamerism favors broadband primaries. To characterize the tradeoffs between the different attributes in primary design for three primary and multiprimary displays, we propose a Pareto optimization framework for determining the complete range of available primary choices that optimally negotiate the tradeoffs between the metrics for the different attributes. Using results obtained in our proposed framework, we explore the impact of number of primaries, the relation between alternative design objectives, and the underlying primary spectral characteristics. The proposed strategy is more informative and comprehensive for primary design and primary selection, and can also be extended to co-optimize primary design and selection of control values to fully leverage the advantages of multiprimary displays.