Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays

TL;DR

This paper introduces an unsupervised deep learning method for contrast enhancement in OLED displays that reduces power consumption by brightness control while maintaining image quality, outperforming traditional techniques.

Contribution

It presents a novel CNN-based PCCE scheme that learns without reference images and effectively balances power savings with visual quality preservation.

Findings

Outperforms conventional contrast enhancement methods in quality metrics

Effectively reduces power consumption through brightness control

Achieves high visual quality with unsupervised learning

Abstract

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).