HDR Display Quality Evaluation by incorporating Perceptual Component Models into a Machine Learning framework

Abstract We present an approach to predict overall high dynamic range (HDR) display quality as a function of key HDR display parameters. Subjective experiments on a high quality HDR display tested five key HDR display parameters: (maximum luminance, minimum luminance, color gamut, bit-depth and local contrast). Two models were explored– a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system (HVS) models. For the perceptual model, we used a family of metrics based on a recently published color space (IC T C P ), as well as an estimate of the display point spread function. To predict the overall visual quality ratings, we compared linear regression and various machine learning (ML) techniques. We find that the perceptual model is better at predicting subjective quality than the physical model and that ML techniques are better at prediction than linear regression. We also investigate the significance and contribution of each display parameter for the combined model. Further, we explore how well these models perform when applied to display capabilities outside of the training data set, both in terms of extrapolation and interpolation. The use of the perceptual transforms particularly helps with extrapolation, and without their tempering effects, the ML-based models can produce wildly unrealistic quality predictions.