Saliency-Guided Deep Framework for Power Consumption Suppressing on Mobile Devices

Abstract

With the growing concern for power-hungry on mobile devices, many power constrained contrast enhancement algorithms have been developed in the mobile devices embedded with emissive displays, such as organic light-emitting diodes. However, conventional power constrained contrast enhancement algorithms inevitably degrade the visual aesthetics of images as a trade-off to gain the power-saving for mobile devices. This paper proposes a trainable power-constrained contrast enhancement algorithm based on a saliency-guided deep framework for suppressing the power consumption of an image while preserving its perceptual quality. Our algorithm relies on the fact that imaging features of a displayed image is salient to human visual perception. Hence, we decompose the input image into the imaging features and textual features with a deep convolutional neural networks, and degrade those textual features to achieve the suppression of power consumption. Experimental results demonstrate that our algorithm is able to maintain visual aesthetics of images while reducing the power consumption effectively, outperforming conventional power-constrained contrast enhancement algorithms.