AdaEnlight : Energy-aware Low-light Video Stream Enhancement on Mobile Devices

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Scopus Citations
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  • Sicong LIU
  • Xiaochen LI
  • Bin GUO
  • Meng ZHANG
  • Haocheng SHEN
  • Zhiwen YU

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Original languageEnglish
Article number172
Journal / PublicationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number4
Publication statusPublished - Dec 2022



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. © 2022 Association for Computing Machinery.

Research Area(s)

  • energy awareness, low light video enhancement, mobile devices

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