Practicalities of predicting quality of high dynamic range images and video

© 2016 IEEE.The paper discusses the use of existing metrics, such as HDR-VDP and extensions of MS-SSIM and PSNR, for prediction of quality in high dynamic range (HDR) images and video. The discussion is based on the experience in using those metrics to evaluate and improve image compression for the new JPEG XT standard, and video compression for the LumaHDR open source codec. The paper explains why existing non-HDR metrics perform very poorly on HDR data and how to improve their predictions. Since most HDR metrics require calibrated data, intended for an HDR display, such calibration step is explained. One of the popular HDR quality metrics, HDR-VDP, is briefly introduced with the update on the latest improvements. Finally, several studies comparing objective HDR metric performance are summarized

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