Zhang Huiqing, Li Donghao, Yu Yibing, Guo Nan
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China.
Entropy (Basel). 2021 Jun 26;23(7):814. doi: 10.3390/e23070814.
In recent years, people's daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people's daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user's experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products' screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products' screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD.
近年来,人们的日常生活已离不开各种电子设备,尤其是手机,手机无疑已成为人们日常生活中的必需品。在本文中,我们正在寻找一种可靠的方法来获取显示产品的视觉质量,以便我们能够改善用户对显示产品的体验。本文提出了两大贡献:第一个是建立了一个新的显示产品屏幕图像主观评估数据库(DPQAD)。具体而言,我们邀请了57位没有经验的观察者对150张展示显示产品的屏幕图像进行评分。同时,为了提高屏幕显示质量评分的可靠性,我们将单刺激法与刺激比较法相结合,有效地评估了新创建的显示产品屏幕图像数据库。第二个是开发了一种新的无参考图像质量评估(IQA)指标。对于给定的显示产品图像,我们的方法首先通过分析对比度、清晰度、亮度等提取27个特征,然后使用回归模块获得视觉质量分数。综合实验表明,我们的方法可以同时评估自然场景图像和屏幕内容图像。此外,与十种最先进的IQA方法相比,我们的方法在DPQAD上表现出明显的优势。