  Increasing popularity of high-dynamic-range (HDR) image and video content
brings the need for metrics that could predict the severity of image
impairments as seen on displays of different brightness levels and dynamic
range. Such metrics should be trained and validated on a sufficiently large
subjective image quality dataset to ensure robust performance. As the existing
HDR quality datasets are limited in size, we created a Unified Photometric
Image Quality dataset (UPIQ) with over 4,000 images by realigning and merging
existing HDR and standard-dynamic-range (SDR) datasets. The realigned quality
scores share the same unified quality scale across all datasets. Such
realignment was achieved by collecting additional cross-dataset quality
comparisons and re-scaling data with a psychometric scaling method. Images in
the proposed dataset are represented in absolute photometric and colorimetric
units, corresponding to light emitted from a display. We use the new dataset to
retrain existing HDR metrics and show that the dataset is sufficiently large
for training deep architectures. We show the utility of the dataset on
brightness aware image compression.
