DEEP-LEARNING-BASED ENERGY AWARE IMAGES

被引：0
|
作者：
Le Meur, Olivier [1 ]
Demarty, Claire-Helene [1 ]
Blonde, Laurent [1 ]
机构：
[1] Interdigital, Cesson Sevigne, France
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP | 2023年
关键词：
energy reduction; power; deep network;
D O I：
10.1109/ICIP49359.2023.10222188
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：590 / 594
页数：5
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