  The ubiquity of camera-embedded devices and the advances in deep learning
have stimulated various intelligent mobile video applications. These
applications often demand on-device processing of video streams to deliver
real-time, high-quality services for privacy and robustness concerns. However,
the performance of these applications is constrained by the raw video streams,
which tend to be taken with small-aperture cameras of ubiquitous mobile
platforms in dim light. Despite extensive low-light video enhancement
solutions, they are unfit for deployment to mobile devices due to their complex
models and and ignorance of system dynamics like energy budgets. In this paper,
we propose AdaEnlight, an energy-aware low-light video stream enhancement
system on mobile devices. It achieves real-time video enhancement with
competitive visual quality while allowing runtime behavior adaptation to the
platform-imposed dynamic energy budgets. We report extensive experiments on
diverse datasets, scenarios, and platforms and demonstrate the superiority of
AdaEnlight compared with state-of-the-art low-light image and video enhancement
solutions.
