Fully automated OLED display power modeling for mobile devices

Yonghun Choi, Rhan Ha, Hojung Cha

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The power consumption of an OLED (Organic Light-Emitting Diode) display depends on the color distribution of the image contents. Previous studies predicted OLED display power using a pixel color-based linear model. In recent mobile devices, however, the relationship between a color and its power consumption has become complicated, due to the various options for display settings in the device and to device diversity. This makes it hard to predict the power consumption of a display accurately with the conventional linear model. In this paper, we propose a technique to automatically generate an OLED display power model optimized for a specific target device, as well as for each display mode, thus generating an accurate power model effectively. The technique automatically learns the power model through the RGB value of each pixel and its power consumption using Support Vector Regression (SVR)-based supervised learning. We evaluated the power models for recent smartphone models, and the results show that, on average, the accuracy of the pixel modeling is about 99% for the device models and display modes used in the experiment. Furthermore, the power consumption for real images is estimated with an accuracy of about 95%, on average.

Original languageEnglish
Pages (from-to)41-55
Number of pages15
JournalPervasive and Mobile Computing
Volume50
DOIs
Publication statusPublished - 2018 Oct 1

Fingerprint

Organic Light-emitting Diodes
Organic light emitting diodes (OLED)
Mobile devices
Mobile Devices
Display
Display devices
Power Consumption
Electric power utilization
Modeling
Pixel
Pixels
Color
Linear Model
Model
Support Vector Regression
Supervised Learning
Smartphones
Supervised learning
Predict
Target

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Applied Mathematics

Cite this

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title = "Fully automated OLED display power modeling for mobile devices",
abstract = "The power consumption of an OLED (Organic Light-Emitting Diode) display depends on the color distribution of the image contents. Previous studies predicted OLED display power using a pixel color-based linear model. In recent mobile devices, however, the relationship between a color and its power consumption has become complicated, due to the various options for display settings in the device and to device diversity. This makes it hard to predict the power consumption of a display accurately with the conventional linear model. In this paper, we propose a technique to automatically generate an OLED display power model optimized for a specific target device, as well as for each display mode, thus generating an accurate power model effectively. The technique automatically learns the power model through the RGB value of each pixel and its power consumption using Support Vector Regression (SVR)-based supervised learning. We evaluated the power models for recent smartphone models, and the results show that, on average, the accuracy of the pixel modeling is about 99{\%} for the device models and display modes used in the experiment. Furthermore, the power consumption for real images is estimated with an accuracy of about 95{\%}, on average.",
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Fully automated OLED display power modeling for mobile devices. / Choi, Yonghun; Ha, Rhan; Cha, Hojung.

In: Pervasive and Mobile Computing, Vol. 50, 01.10.2018, p. 41-55.

Research output: Contribution to journalArticle

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