An interpretable metric of visual aesthetics for GUI design.
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| Title: | An interpretable metric of visual aesthetics for GUI design. |
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| Authors: | Wang, Chen, Miniukovich, Aliaksei, Ren, Xiangshi |
| Source: | Behaviour & Information Technology. Feb2025, Vol. 44 Issue 3, p508-522. 15p. |
| Subjects: | Pearson correlation (Statistics), Graphical user interfaces, Aesthetics, Research funding, Art, Descriptive statistics, Creative ability, Web development, Intraclass correlation, Visual perception, User interfaces, HTML (Document markup language) |
| Abstract: | Computation-based aesthetics metrics have been developed to help designers predict visual aesthetics scores for GUI design. However, designers find these evaluative scores difficult to understand. This paper proposed an interpretable aesthetics metric for GUI design that integrates visual aesthetics (visual similarity and spatial proximity) and GUI structure (semantic similarity and white space) to model visual grouping distribution. Two experiments were conducted to validate the metric's ability to predict aesthetics and interpret outputs. Experiment 1 showed that our metric had a stronger correlation with users' impressions of GUI visual aesthetics than past metrics. Experiment 2 suggested that our metric was easier to interpret and appeared more useful to Visual/Graphic/GUI designers than a conventional score-based alternative, by visualising the metric outputs as an experimental tool. Furthermore, this paper provided five potential insights to further advance computational aesthetics research. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Computation-based aesthetics metrics have been developed to help designers predict visual aesthetics scores for GUI design. However, designers find these evaluative scores difficult to understand. This paper proposed an interpretable aesthetics metric for GUI design that integrates visual aesthetics (visual similarity and spatial proximity) and GUI structure (semantic similarity and white space) to model visual grouping distribution. Two experiments were conducted to validate the metric's ability to predict aesthetics and interpret outputs. Experiment 1 showed that our metric had a stronger correlation with users' impressions of GUI visual aesthetics than past metrics. Experiment 2 suggested that our metric was easier to interpret and appeared more useful to Visual/Graphic/GUI designers than a conventional score-based alternative, by visualising the metric outputs as an experimental tool. Furthermore, this paper provided five potential insights to further advance computational aesthetics research. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 0144929X |
| DOI: | 10.1080/0144929X.2024.2325030 |
