Exploiting Machine-learning Prediction for Enabling Real-time Pixel-scaling Techniques in Mobile Camera Applications

Abstract

Modern people are used to recording more and more videos using camera applications for keeping and sharing their life on social media and video-sharing platforms. To capture extensive multimedia materials, reducing the power consumption of recorded videos from camera applications plays an important role for user experience of mobile devices. This paper studies how to process and display power-saving videos recorded by camera applications on mobile devices in a real-time manner. Based on pixel-scaling methods, we design an appropriate feature map and adopt a visual attention model under the real-time limitation to effectively access attention distribution. Then, based on segmentation properties, a parallel design is appropriately applied to exploit available computation power. Next, we propose a frame-ratio predictor using machine-learning methods to efficiently predict frame ratios in a frame. Finally, the results of the comprehensive experiments conducted on a commercial smartphone with four real-world videos to evaluate the performance of the proposed design are very encouraging.