Various power-constrained contrast enhance-ment (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the pow-er demands of the display while preserving the image qual-ity. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power con-sumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is pre-served as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE technique without a reference image by unsupervised learning. Ex-perimental results show that the proposed method is supe-rior to conventional ones in terms of image quality assess-ment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).1.

中文翻译：

---

OLED 显示器具有功率限制的无监督深度对比度增强。


各种功率约束对比度增强（PCCE）技术已应用于有机发光二极管（OLED）显示器，以在保持图像质量的同时降低显示器的功率需求。在本文中，我们提出了一种新的基于深度学习的 PCCE 方案，该方案限制 OLED 显示器的功耗，同时增强显示图像的对比度。在所提出的方法中，通过简单地将亮度降低一定比例来限制功耗，而通过使用卷积神经网络（CNN）增强图像的对比度来尽可能保留感知的视觉质量。此外，我们的 CNN 可以通过无监督学习在没有参考图像的情况下学习 PCCE 技术。实验结果表明，该方法在视觉显着性诱导指数（VSI）和增强度量（EME）等图像质量评估指标方面优于传统方法。 1．