A High-Definition Diversity-Scene Database for Image Quality Assessment

Abstract

In this paper, we focus on the creation of general purpose 2-D image quality databases. Although there are many of them, they still lack some important characteristics, such as high-definition resolution, diversified source images, more commonly seen distortions, and a larger amount of test (distorted) images. To tackle this problem, we create a high-definition image database, which has higher resolution than most of the image quality databases. In addition, we collect 250 source images from 10 categories, which are far more diversified than other existing quality databases. Moreover, we generate 10 most commonly seen distortions to represent the real world scenario. Finally, 12000 test images are generated for the whole database, which is the largest data set so far compared with other general purpose image quality databases with human subjective ratings. The subjective test is conducted in a controlled environment to obtain the ground truth of image quality, where we collect over 360000 opinion scores. We believe the birth of this quality database would help further development of research on image quality assessment.