The DisplayPower2k dataset is a set of power consumption measures for different OLED screens, and pixel-wise power map images, computed from 648 images.

Additional energy-aware images and their corresponding power measures are also distributed together with the DisplayPower2k dataset.

The dataset is intended to be used for training and assessing models to predict and control the power consumption of images when displayed on screens. A detailed description of the benchmark can be found on our Data Description page. The license conditions are mentioned on the Download page.

Citing the DisplayPower2k dataset

All documents and papers that report on research that uses the DisplayPower2k Dataset must acknowledge the use of the dataset by including an appropriate citation to the followings:

Claire-Hélène Demarty, Laurent Blondé, Olivier Le Meur. "Display Power Modeling for Energy Consumption Control." ICIP (2023).

Olivier Le Meur, Claire-Hélène Demarty, Laurent Blondé. "Deep-Learning-based Energy Aware Images." ICIP (2023).

It would be highly appreciated if this use was shared with InterDigital, at this address: displaypowermanagement@interdigital.com, with “DisplayPower2k” in the subject of your email.

The DisplayPower2k Dataset contains power measures for different OLED screens and pixel-wise power map images.

Power measures were collected on three different OLED displays: SONY KD 55AF9, LG 42C2 and LG 65E6V. For each screen, standard image mode was used, color temperature and gamma were adjusted to get as close as possible to D65 white, and all other possible settings were disabled, wherever possible. R, G, B gains were also adjusted as much as possible to limit the use of the R/G/B leds when displaying a white image for the two first screens.

All measures were done in a dark room to avoid the automatic adaptation to ambient light, with a Konica Minolta CS 200 chromameter and a Voltcraft Energy Logger 4000F wattmeter.

Measurements were done on a first set of 96 uniform images and a second set of 552 real images of different resolutions and quality from 7 sub-datasets: historical, Inria-holidays, Urban100, BSDS100, Manga109s, ITU-R, Set14+Set5 [1, 2, 3, 4, 5, 6, 7, 8].

This led to 648 measurements per screen, i.e., a total of 1944 power measures.

For each 96 uniform images, CIE-Yxy values measured with the chromameter are also available.

1296 pixel-wise power maps computed on the SONY and LG 42C2 screens complement the dataset. They were computed by running the theoretical model proposed in our ICIP paper [9].

All original images should be downloaded from the official links provided in the below references.

Images from historical dataset can be downloaded from:

https://github.com/andreas128/mmsr/blob/master/datasets/DATASETS.md

https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU

The DisplayPower2K dataset therefore contains:

• 96 uniform images;
• Their corresponding 96 Yxy measured values;
• 1944 power measures on 3 different screens from 648 images;
• 1296 pixel-wise power map images on 2 different screens from 648 images.

Note: In this version of the dataset, power measures and power maps are provided for all datasets except Manga109s.

In the context of the paper “Deep-Learning-based Energy Aware Images” (ICIP 2023)[10], additional energy aware images and corresponding power measures were also computed for the SONY screen. They are released in the current version of the DisplayPower2k dataset.

References

[1] H. Jegou, M. Douze, and C Schmid, “Hamming embedding and weak geometry consistency for large scale image search,” in Proceedings of the 10th European conference on Computer vision (ECCV 2008), 2008.

[2] Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa, “Sketch-based manga retrieval using manga109 dataset,” Multimedia Tools and Applications,vol. 76, no. 20, pp. 21811–21838, 2017.

[3] Kiyoharu Aizawa, Azuma Fujimoto, Atsushi Otsubo,Toru Ogawa, Yusuke Matsui, Koki Tsubota, and Hikaru Ikuta, “Building a manga dataset “manga109” with annotations for multimedia applications,” IEEE MultiMedia, vol. 27, no. 2, pp. 8–18, 2020.

[4] M. Bevilacqua, A. Roumy, C. Guillemot, and ML. Alberi, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” in Proceedings of the British Machine Vision Conference (BMVC 2012), 2012. [5] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, “Single image super-resolution from transformed self-exemplars,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5197–5206.

[6] D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. 8th Int’l Conf. Computer Vision, July 2001, vol. 2, pp. 416–423.

[7] ITU-R, “HDTV and UHDTV including HDR-TV test materials for assessment of picture quality,” Tech. Rep., ITU-R BT.2245-10, 2022.

[8] R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse representations,” in Proceedings of the 7th international conference on Curves and Surfaces, 2010, pp. 711–730.

[9] Claire-Hélène Demarty, Laurent Blondé, Olivier Le Meur. "Display Power Modeling for Energy Consumption Control." ICIP (2023).

[10] Olivier Le Meur, Claire-Hélène Demarty, Laurent Blondé. "Deep-Learning-based Energy Aware Images." ICIP (2023).

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To download the DisplayPower2k Dataset please send an email to displaypowermanagement@interdigital.com. By doing this you irrevocably agree to any and all provision of the license agreement on this page.

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Claire-Hélène Demarty, Laurent Blondé, Olivier Le Meur. "Display Power Modeling for Energy Consumption Control." ICIP (2023).

Olivier Le Meur, Claire-Hélène Demarty, Laurent Blondé. "Deep-Learning-based Energy Aware Images." ICIP (2023).

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