Due to the problems of power-hungry displays and limited battery life in electronic devices, the concept of “green computing,” which entails a reduction in power consumption, is proposed. One often seen green computing is the power-constrained contrast enhancement (PCCE), yet it is much more challenging because of the noticeable local intensity suppressions in images. This paper aims at developing an image-quality lossless end-to-end learning network called deep battery saver to achieve power savings in emissive displays, i.e., produce power-saved images with high perceptual quality and less power consumption. Built upon the end-to-end network of the displayed image, we propose a variational loss function for enhancing the visual quality and suppressing the power consumption, simultaneously. The basic idea is to integrate both high-level perceptual losses and low-level pixel losses by a deep residual convolutional neural network (CNN) over a devised variational loss function with strong human perceptual consistency. Such deep residual CNN network leads to a visually pleasing image representation during the suppression of power consumption. Experimental results demonstrated the superiority of our deep battery saver to existing PCCE methods.

中文翻译：

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Deep Battery Saver：用于功率受限对比度增强的端到端学习

由于电子设备的显示器耗电和电池寿命有限的问题，提出了降低功耗的“绿色计算”概念。一种常见的绿色计算是功率受限对比度增强 (PCCE)，但由于图像中明显的局部强度抑制，它更具挑战性。本文旨在开发一种称为深度省电的图像质量无损端到端学习网络，以实现发光显示器的节能，即生成具有高感知质量和更低功耗的节能图像。基于显示图像的端到端网络，我们提出了一种变分损失函数，用于同时提高视觉质量和抑制功耗。基本思想是通过深度残差卷积神经网络 (CNN) 在设计的具有强人类感知一致性的变分损失函数上整合高级感知损失和低级像素损失。在抑制功耗的过程中，这种深度残差 CNN 网络导致视觉上令人愉悦的图像表示。实验结果证明了我们的深度电池保护程序优于现有的 PCCE 方法。