CAPED: context-aware personalized display brightness for mobile devices

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, C ontext- A ware PE rsonalized D isplay (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.