Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays

Yong Goo Shin, Seung Park, Yoon Jae Yeo, Min Jae Yoo, Sung Jea Ko

Research output: Contribution to journalArticle

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).

Original languageEnglish
Article number8906230
Pages (from-to)2834-2844
Number of pages11
JournalIEEE Transactions on Image Processing
Volume29
DOIs
Publication statusPublished - 2020 Jan 1

Keywords

  • Convolutional neural network
  • deep learning
  • energy efficiency
  • image enhancement

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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