High Resolution Image Quality Database
Huang Huang Qiang Wan Jari Korhonen
With technology for digital photography and high resolution displays rapidly
evolving and gaining popularity, there is a growing demand for blind image
quality assessment (BIQA) models for high resolution images. Unfortunately, the
publicly available large scale image quality databases used for training BIQA
models contain mostly low or general resolution images. Since image resizing
affects image quality, we assume that the accuracy of BIQA models trained on
low resolution images would not be optimal for high resolution images.
Therefore, we created a new high resolution image quality database (HRIQ),
consisting of 1120 images with resolution of 2880x2160 pixels. We conducted a
subjective study to collect the subjective quality ratings for HRIQ in a
controlled laboratory setting, resulting in accurate MOS at high resolution. To
demonstrate the importance of a high resolution image quality database for
training BIQA models to predict mean opinion scores (MOS) of high resolution
images accurately, we trained and tested several traditional and deep learning
based BIQA methods on different resolution versions of our database. The
database is publicly available in https://github.com/jarikorhonen/hriq.