Machine learning as applied intrinsically to individual dimensions of HDR Display Quality

This study builds on previous work exploring machine learning and perceptual transforms in predicting overall display quality as a function of image quality dimensions that correspond to physical display parameters. Previously, we found that the use of perceptually transformed parameters or machine learning exceeded the performance of predictors using just physical parameters and linear regression. Further, the combination of perceptually transformed parameters with machine learning allowed for robustness to parameters outside of the data set, both for cases of interpolation and extrapolation. Here we apply machine learning at a more intrinsic level. We first evaluate how well the machine learning can develop predictors of the individual dimensions of the overall quality, and then how well those individual predictors can be consolidated across themselves to predict the overall display quality. Having predictions of individual dimensions of quality that are closely related to specific hardware design choices enables more nimble cost trade-off design options.