Quantification of interface visual complexity

Abstract

Designers strive for enjoyable user experience (UX) and put a significant effort into making graphical user interfaces (GUI) both usable and beautiful. Our goal is to minimize their effort: with this purpose in mind, we have been studying automatic metrics of GUI qualities. These metrics could enable designers to iterate their designs more quickly. We started from the psychological findings that people tend to prefer simpler things. We then assumed visual complexity determinants also determine visual aesthetics and outlined eight of them as belonging to three dimensions: information amount (visual clutter and color variability), information organization (symmetry, grid, ease-of-grouping and prototypicality), and information discriminability (contour density and figure-ground contrast). We investigated five determinants (visual clutter, symmetry, contour density, figure-ground contrast and color variability) and proposed six associated automatic metrics. These metrics take screenshots of GUI as input and can thus be applied to any type of GUI. We validated the metrics through a user study: we gathered the ratings of immediate impressions of GUI visual complexity and aesthetics, and correlated them with the output of the metrics. The output explained up to 51 of aesthetics ratings and 50 of complexity ratings. This promising result could be further extended towards the creation of tLight, our automatic GUI evaluation tool

Visual impressions of mobile app interfaces

“…The best-fit regression models explained 40% of subjective complexity evaluation and 36% of aesthetics evaluation. These two studies showed how app GUI screenshots could be used for automatic GUI evaluation and extended our initial effort [21] towards the development of an automatic GUI evaluation tool. In the rest of the paper, we review related work on visual complexity, the measures of visual complexity and specificity of mobile design.…”

“…Researchers [4,19,35,37,38,43] mainly concentrated on desktop versions of websites and their various interaction qualities, including aesthetics. Positive initial impressions are thought to originate from high processing fluency [29,28]: people like simple interfaces that they understand quickly [37,21]. Smartphone users might value simplicity even more as the on-the-go usage implies multiple external distractions, and short and intensive interaction periods [8].…”

User Satisfaction Determinants for Digital Culture Heritage Online Collections

“…Usability, learnability, functionality, accessibility and ease of use are the factors to influence overall user satisfaction [19]. Aesthetic, [20] interface design and joyful of use also could influence the factors [21]. Table II below displays the summaries of the factors to influence user satisfaction according to 6 different domains.…”

Towards a catalog of usability smells

“…The use of such good practices also makes it easier (or possible) to maintain and evolve such complex systems. Although there are advanced interactive frameworks, like for example WebRatio 1 , Symfony 2 , and outSystems 3 , and approaches that provide powerful abstraction mechanisms to develop and test interactive applications (e.g., [7,4,15,23]), the reality is that poorly designed applications are still far too common. In [12] a survey of common bad designs of interactive applications is presented.…”

“…In [12] a survey of common bad designs of interactive applications is presented. Techniques and tools to quantify the complexity interactive applications are proposed in [15,23]. Techniques to eliminate detected anomalies are defined in [5,7].…”