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.

翻译：随着数字摄影和高分辨率显示技术的快速发展和普及，针对高分辨率图像的无参考图像质量评估（BIQA）模型需求日益增长。然而，当前用于训练BIQA模型的公开大规模图像质量数据库大多包含低分辨率或常规分辨率图像。由于图像缩放会影响图像质量，我们推测基于低分辨率图像训练的BIQA模型对高分辨率图像的准确性并非最优。为此，我们构建了新的高分辨率图像质量数据库（HRIQ），包含1120幅分辨率为2880×2160像素的图像。我们在受控实验室环境中开展了主观研究以收集HRIQ的主观质量评分，获得了高分辨率下准确的MOS值。为证明高分辨率图像质量数据库对训练BIQA模型准确预测高分辨率图像平均意见得分（MOS）的重要性，我们在数据库的不同分辨率版本上训练并测试了多种传统及基于深度学习的BIQA方法。该数据库已在https://github.com/jarikorhonen/hriq 公开分享。