A comprehensive appraisal of perceptual visual complexity analysis methods in GUI design

Akça, Eren; Tanrıöver, Ömer Özgür

doi:10.1016/j.displa.2021.102031

Cited by 6 publications

(3 citation statements)

References 62 publications

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“…Because visual complexity is quantifiable, it can be manipulated quantitatively [e.g. [58]]. The simple parameter was defined by deduction, during an extensive design research phase where various graphical styles and sketches were created.…”

Section: Complexity Characteristic: Designing Icon "Flesh" By Applyin...mentioning

confidence: 99%

Design standards for icons: The independent role of aesthetics, visual complexity and concreteness in icon design and icon understanding

et al. 2022

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Section: Complexity Characteristic: Designing Icon "Flesh" By Applyin...mentioning

confidence: 99%

Design standards for icons: The independent role of aesthetics, visual complexity and concreteness in icon design and icon understanding

et al. 2022

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“…Although computer vision techniques have attracted attention in many fields [19][20][21], metrics related to the components and perceptual attributes of graphical user interfaces has been used in the field of measuring visual complexity so far [22]. In a recent study, Michailidou et al [23] aimed to develop a prediction model that calculates the visual complexity value of web pages through the source codes of the web pages.…”

Section: Related Workmentioning

confidence: 99%

“…From another point of view, Soui et al [7] aimed to solve the visual complexity analysis problem for mobile UIs by transforming it into a multi-objective optimization problem. In a recent study, Akç a and Tanriöver [22] reviewed attributes affecting the visual complexity of UIs and presented a taxonomy of the visual complexity analysis methods, where each method was discussed in detail.…”

Section: Related Workmentioning

confidence: 99%

A Deep Transfer Learning Based Visual Complexity Evaluation Approach to Mobile User Interfaces

Akça¹,

Tanrıöver²

2022

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Visual complexity is an important factor affecting the efficiency and functionality of user interfaces. Its impact on the user's impression and the usability is significant, especially for mobile applications with constraints such as layout size, on screen keys and small input fields. Conventional approaches for visual complexity evaluation of user interfaces are either based on user evaluations with surveys or based on pre-specified formal metrics or on heuristics. Alternatively, in this study, we have explored the effectiveness of deep learning models for visual complexity evaluation, specifically, of mobile user interfaces. We have experimented with five state of the art pre-trained deep learning models known to be effective for computer vision tasks, namely, VGG16, DenseNet121, MobileNetv2, GoogleNet and ResNet152 were trained with 3635 different mobile user interface images as login, menu, search and settings. Furthermore, in order to validate the effectiveness of this approach, a new validation dataset and survey application was developed and an evaluation study was conducted with 98 participants where 7309 comparison result were obtained from the study. It was found that the agreement rate between the results of deep learning models and the user evaluations was up to 78% and 74% on the average. The high to moderate agreement rate between the results of deep learning models and the user evaluations reveals that this approach can be useful for designers in visual complexity evaluation of mobile user interfaces.

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Color aesthetics with regard to product design and multimedia web pages

Kuo

Chang²,

Chen

2023

Multimed Tools Appl

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A comprehensive appraisal of perceptual visual complexity analysis methods in GUI design

Cited by 6 publications

References 62 publications

Design standards for icons: The independent role of aesthetics, visual complexity and concreteness in icon design and icon understanding

Design standards for icons: The independent role of aesthetics, visual complexity and concreteness in icon design and icon understanding

A Deep Transfer Learning Based Visual Complexity Evaluation Approach to Mobile User Interfaces

Color aesthetics with regard to product design and multimedia web pages

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