CAPED: Context-aware personalized display brightness for mobile devices
CAPED: Context-aware personalized display brightness for mobile devices
复制标题
CAPED：移动设备的上下文感知个性化显示亮度
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
G. Memik
中科院分区：
文献类型：
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作者：
Matthew Schuchhardt;Susmit Jha;R. Ayoub;M. Kishinevsky;G. Memik
显示器仍然是许多计算设备上的主要用户界面，范围从诸如台式机和膝上型计算机的传统设备到诸如智能电话和智能手表的更普遍的设备。因此，这些计算设备的总体用户体验在很大程度上由显示子系统确定。理想的显示亮度对于良好的用户体验至关重要，但实际上预测最能满足用户的理想亮度水平是一个挑战。在移动的设备上找到合适的屏幕亮度更具挑战性（这是这项工作的重点），因为屏幕往往是最耗电的组件之一。目前，显示器亮度的控制通常通过简单的、静态的一刀切的模型来完成，该模型针对给定的环境光条件选择固定的亮度水平。我们的用户研究和调查的研究文献的视觉和感知建立，目前用于显示器亮度控制的简单模型是不够的。理想的显示亮度水平因用户而异。此外，除了环境光之外，我们还确定了其他影响理想亮度的上下文数据。我们提出了一个新的系统，上下文感知的Personalized显示（CAPED），使用在线学习来控制显示亮度，并在理论上和实践证明，随着时间的推移，以提高预测精度。CAPED可实现亮度控制的个性化以及利用更丰富的上下文数据来更好地预测正确的显示亮度。我们的用户研究表明，CAPED改进了最先进的亮度控制技术，平均绝对预测精度提高了41.9%。我们的用户研究还表明，在5分制中，用户的满意度平均高出0.8分。换句话说，与默认方案相比，CAPED将平均满意度提高了23.5%。
The display remains the primary user interface on many computing devices, ranging from traditional devices such as desktops and laptops, to the more pervasive devices such as smartphones and smartwatches. Thus, the overall user experience with these computing devices is greatly determined by the display subsystem. Ideal display brightness is critical to good user experience, but actually predicting the ideal brightness level which would most satisfy the user is a challenge. Finding the right screen brightness is even more challenging on mobile devices (which is the focus of this work), as the screen tends to be one of the most power consuming components. Currently, the control of display brightness is usually done through a simplistic, static one-size-fits-all model which chooses a fixed brightness level for a given ambient light condition. Our user study and survey of research literature on vision and perception establish that the simplistic model currently used for display brightness control is not sufficient. The ideal display brightness level varies from one user to another. Furthermore, in addition to ambient light, we identify additional contextual data that also affect the ideal brightness. We propose a new system, Context-Aware PErsonalized Display (CAPED), that uses online learning to control the display brightness, and is theoretically and practically shown to improve prediction accuracy over time. CAPED enables personalization of brightness control as well as exploitation of richer contextual data to better predict the right display brightness. Our user study shows that CAPED improves the state-of-the-art brightness control techniques with a 41.9% improvement in mean absolute prediction accuracy. Our user study also shows that on average the users had 0.8 point higher satisfaction on a 5-point scale. In other words, CAPED improves the average satisfaction by 23.5% compared to the default scheme.