Unsupervised Deep Contrast Enhancement With Power Constraint for OLED Displays

• Published in: IEEE transactions on image processing Vol. 29; pp. 2834 - 2844
• Main Authors: Shin, Yong-Goo, Park, Seung, Yeo, Yoon-Jae, Yoo, Min-Jae, Ko, Sung-Jea
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brightness; Constraints; Convolutional neural network; deep learning; Displays; energy efficiency; Hardware; Image contrast; Image enhancement; Image quality; Machine learning; Organic light emitting diodes; Power consumption; Power demand; Quality assessment; Visual perception; Visualization
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2019.2953352

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).