Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality

TL;DR

This paper introduces a large, unified HDR image quality dataset (UPIQ) with over 4,000 images, enabling improved training of quality metrics and demonstrating applications like brightness-aware image compression.

Contribution

The creation of the UPIQ dataset, merging and realigning existing HDR and SDR datasets with a unified quality scale, facilitating robust HDR quality assessment and deep learning training.

Findings

The dataset enables effective training of deep HDR quality metrics.

Unified quality scores improve cross-dataset consistency.

Application to brightness-aware image compression shows practical benefits.

Abstract

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.