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A comprehensive appraisal of perceptual visual complexity analysis methods in GUI design
Displays ( IF 4.3 ) Pub Date : 2021-06-14 , DOI: 10.1016/j.displa.2021.102031
Eren Akça , Ömer Özgür Tanriöver

Graphical or Visual User Interface (GUI) is recognized as one of the most important application components for safety critical and business oriented software systems. It is highly advantageous for GUI designers and application developers to analyze the visual complexity of a GUI and predict users’ perception and judgment during the design phase. Although in recent years, various methods have been developed for visual complexity analysis, these have not been widely used due to applicability, practicality and validity issues. In this respect, we have conducted a comprehensive review of studies and methods in visual complexity analysis. After identifying and analyzing 85 research studies, we grouped the visual complexity analysis methods and accordingly a taxonomy is presented. Furthermore, conceptual comparison of the methods is given and gap analysis as well as possible future directions are provided. According to the our findings, major gaps for each visual complexity analysis method may be stated as follows: 1) In metric-model based methods, there is a lack of information about the suitability of the metric-model created for analysis, since the extent to which each metric contributes to visual complexity analysis is still not known exactly. 2) In heuristic- based methods, the extracted rule set is not yet extendable enough beyond the use for specific GUIs. 3) While the visual complexity analysis could be considered as a kind of computer vision task, there exist limited studies that does so. Therefore, generalizable solutions based on machine learning techniques seem to be a promising research direction to develop efficient approaches.



中文翻译:

GUI设计中感知视觉复杂度分析方法的综合评价

图形或可视用户界面 (GUI) 被公认为安全关键和面向业务的软件系统最重要的应用组件之一。对于 GUI 设计人员和应用程序开发人员来说,在设计阶段分析 GUI 的视觉复杂性并预测用户的感知和判断是非常有利的。尽管近年来已经开发了各种用于视觉复杂性分析的方法,但由于适用性、实用性和有效性问题,这些方法并未得到广泛应用。在这方面,我们对视觉复杂性分析的研究和方法进行了全面审查。在识别和分析 85 项研究之后,我们对视觉复杂性分析方法进行了分组,并相应地提出了分类法。此外,给出了这些方法的概念比较,并提供了差距分析以及未来可能的方向。根据我们的发现,每种视觉复杂性分析方法的主要差距可能如下:1)在基于度量模型的方法中,缺乏有关为分析创建的度量模型的适用性的信息,因为范围每个指标对视觉复杂性分析的贡献尚不清楚。2) 在基于启发式的方法中,提取的规则集还不能扩展到超出特定 GUI 的使用范围。3)虽然视觉复杂性分析可以被视为一种计算机视觉任务,但这样做的研究有限。所以,

更新日期:2021-06-14
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