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A Real-Time Power-Saving Framework for Mobile Camera Applications Based on Machine Learning
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AI
DOI：10.1109/ACCESS.2026.3654116.png)
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Abstract
En 中文
Modern individuals are now accustomed to using mobile device camera applications to capture numerous videos, documenting and sharing their life experiences on various social media and video-sharing platforms. To enhance user experiences when recording extensive multimedia content, it becomes crucial to reduce the power consumption associated with these recorded videos. This paper delves into the real-time processing and display of energy-efficient videos captured using camcorders on mobile devices. We begin by exploring pixel-scaling technologies, developing a representative map, and adopting a suitable visual attention model to assess attention distribution within the constraints of real-time processing. Subsequently, we introduce a frame ratio predictor based on machine learning techniques, enabling the efficient prediction of frame ratios in captured video frames with a high degree of accuracy. To optimize the computational resources available on mobile devices, we leverage parallel design principles while analyzing the segmentation phase’s characteristics. Finally, our comprehensive experiments, conducted on a commercial smartphone using four real-world videos, yield highly encouraging results in terms of processing speed, power-saving capabilities, and visual quality.
Keywords:
Camera applications
real-time design
machine learning methodologies
OLED displays
power-saving technologies
mobile devices