Abstract
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.
Highlights
With the arrival of the era of big data, various terminal electronic devices are needed to support human daily life
We have studied an important but less researched direction, which is subjective and objective quality assessment of display products
The time that humans spend on display products has greatly increased, and there has been an increasing demand for a high-quality visual experience of display screens
Summary
With the arrival of the era of big data, various terminal electronic devices are needed to support human daily life. In order to overcome the above shortcomings, we design a novel no-referenced (NR) quality assessment method of display products (NQMDP), considering that the authors of [9,20] have explained that complexity, contrast, sharpness, brightness, colorfulness, and naturalness are critical to image quality, so we extracted 27 image features based on the above six key factors that affect image quality and learned the above features through SVR to infer the overall quality score of a given image Because this method does not require any reference information, it is very practical in many practical application scenarios.
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