TRAINED PERCEPTUAL TRANSFORM FOR QUALITY ASSESSMENT OF HIGH DYNAMIC RANGE IMAGES AND VIDEO

In this paper, we propose a trained perceptually transform for quality assessment of high dynamic range (HDR) images and video. The transform is used to convert absolute luminance values found in HDR images into perceptually uniform units, which can be used with any standard-dynamic-range metric. The new transform is derived by fitting the parameters of a previously proposed perceptual encoding function to 4 different HDR subjective quality assessment datasets using Bayesian optimization. The new transform combined with a simple peak signal-to-noise ratio measure achieves better prediction performance in cross-dataset validation than existing transforms. We provide Matlab code for our metric(1).