Deep-Learning-Based Energy Aware Images

In this paper, we present a method to compute energy-aware images, that aims to reduce the energy consumption of displays. This method relies on a lightweight unsupervised deep model which finds out the best trade-off between visual quality and energy reduction. From an input image and an energy red...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 590 - 594
Main Authors Le Meur, Olivier, Demarty, Claire-Helene, Blonde, Laurent
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Broadcasting
Computational modeling
deep network
Energy consumption
energy reduction
Image processing
power
Quality of experience
Standardization
Visualization
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Summary:In this paper, we present a method to compute energy-aware images, that aims to reduce the energy consumption of displays. This method relies on a lightweight unsupervised deep model which finds out the best trade-off between visual quality and energy reduction. From an input image and an energy reduction rate, a dimming map is inferred. We show that the proposed model performs as good as state-of-the-art methods, while being much more simple. In addition, the dimming map computation is constrained in order to ease its distribution throughout the video chain.
DOI:10.1109/ICIP49359.2023.10222188