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Unsupervised Deep Contrast Enhancement With Power Constraint for OLED Displays
- Source :
- IEEE Transactions on Image Processing. 29:2834-2844
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
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).<br />Comment: Accepted to IEEE transactions on Image Processing. To be published
- Subjects :
- FOS: Computer and information sciences
Brightness
Computer science
Image quality
Computer Vision and Pattern Recognition (cs.CV)
media_common.quotation_subject
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Convolutional neural network
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
OLED
Contrast (vision)
Computer vision
media_common
business.industry
Deep learning
Image and Video Processing (eess.IV)
Electrical Engineering and Systems Science - Image and Video Processing
Computer Graphics and Computer-Aided Design
Power (physics)
Unsupervised learning
020201 artificial intelligence & image processing
Artificial intelligence
business
Software
Subjects
Details
- ISSN :
- 19410042 and 10577149
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Image Processing
- Accession number :
- edsair.doi.dedup.....a68c46611fd3f9a216768a2980f2a0d1