CAPED: Context-Aware PErsonalized Display brightness for mobile devices

Matthew Schuchhardt*, Susmit Jha, Raid Ayoub, Michael Kishinevsky, Gokhan Memik

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

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. Copyrightc 2014 ACM

Original languageEnglish (US)
Title of host publication2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES 2014
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450330503
DOIs
StatePublished - Oct 12 2014
Event2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES 2014 - New Delhi, India
Duration: Oct 12 2014Oct 17 2014

Publication series

Name2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES 2014

Conference

Conference2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES 2014
Country/TerritoryIndia
CityNew Delhi
Period10/12/1410/17/14

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'CAPED: Context-Aware PErsonalized Display brightness for mobile devices'. Together they form a unique fingerprint.

Cite this