Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays
This work addresses power efficiency for OLED display users, but it is incremental as it builds on existing power-constrained contrast enhancement techniques with a deep learning approach.
The paper tackled the problem of reducing power consumption in OLED displays while preserving image quality by proposing an unsupervised deep learning-based contrast enhancement method that constrains power by reducing brightness and enhances contrast using a CNN. Experimental results showed superiority over conventional methods in metrics like VSI and EME.
Various power-constrained contrast enhancement (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display while preserving the image quality. 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 consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved 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. Experimental results show that the proposed method is superior to conventional ones in terms of image quality assessment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).