When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements' spatial arrangement. We propose a data-driven method that provides flexibility by considering users' preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.
翻译:在设计信息图表时,一般用户通常很难利用现有的信息图表作者工具获得所需的色调调色盘,这些工具有时牺牲了自定义性,需要设计专门知识,或者忽视了元素的空间安排的影响。我们提出了一个数据驱动方法,通过考虑用户的偏好而提供灵活性,通过自动化降低专业知识障碍,裁缝建议将调色板用于元素的空间布局。我们通过利用深层次学习技术来描述数据中良好的颜色设计做法来构建一个建议引擎,并进一步发展InfoColorizer(InfoColorizer)这一工具,使用户能够以互动和动态的方式获取其信息图片的色调盘。为了验证我们的方法,我们进行了四部分综合评价,包括案例研究、受控用户研究、调查研究和访谈研究。结果显示,信息采集器能够以充分的灵活性提供令人信服的调色板建议,使用户能够有效地获得高质量色彩设计,用于低努力量的图像输入。