For an improved user experience, the display sub-system is expected to provide superior resolution and optimal brightness despite its impact on battery life. Existing brightness scaling approaches set the display brightness statically or adaptively in response to predefined events such as low-battery or ambient light of the environment, which are independent of the displayed content. Approaches that consider the displayed content are either limited to video content or do not account for the user's expected battery life, thereby failing to maximise the user experience. This paper proposes Content- and ambient Lighting-aware Adaptive Brightness Scaling in mobile devices that maximises user experience while meeting battery life expectations. The approach employs a content- and ambient lighting-aware profiler that learns and classifies each sample into predefined clusters at runtime by leveraging insights on user perceptions of content and ambient luminance variations. We maximise user experience through adaptive scaling of the display's brightness using an energy prediction model that determines appropriate brightness levels while meeting expected battery life. The evaluation of the proposed approach on a commercial smartphone improves Quality of Experience (QoE) by up to 24.5 % compared to state-of-art.

Display subsystems have become the predominant user interface on mobile devices, serving as both input and output interfaces. For a better quality of user experience (QoE), the display subsystem is expected to provide appropriate resolution and brightness despite its impact on battery life. Existing display brightness approaches either consider content- and ambient-light in isolation or do not account for the user’s expected battery life, thereby failing to maximise the QoE. This paper proposes aCADS, a self-Adaptive Content- and Ambient-aware Display brightness Scaling in mobile devices that maximises QoE while meeting battery life expectations. The approach employs a content- and ambient lighting-aware profiler that learns and classifies each sample into predefined clusters at runtime by leveraging insights on user perceptions of content and ambient luminances variations. We maximise QoE through adaptive scaling of the display’s brightness using an energy model that determines appropriate brightness levels while meeting expected battery life. The evaluation on a commercial smartphone shows that aCADS improves QoE by up to 32.5 % compared to state-of-the-art.