TY  - INPR
TI  - Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
Y1  - 2021/04/29/
AV  - public
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
N2  - Increasing popularity of high-dynamic-range 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 high-dynamic-range 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 high-dynamic-range and standard-dynamic-range 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.
ID  - discovery10128617
UR  - https://doi.org/10.1109/TMM.2021.3076298
JF  - IEEE Transactions on Multimedia
A1  - Mikhailiuk, A
A1  - Perez-Ortiz, M
A1  - Yue, D
A1  - Suen, WS
A1  - Mantiuk, R
KW  - High Dynamic Range
KW  -  Image Quality Dataset
KW  -  Image Quality Metric
ER  -