AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices
This addresses the challenge of deploying low-light enhancement for mobile video applications, which is incremental as it focuses on energy-aware adaptation rather than a new paradigm.
The paper tackles the problem of enhancing low-light video streams on mobile devices in real-time while adapting to dynamic energy budgets, achieving competitive visual quality with demonstrated superiority over state-of-the-art methods.
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.