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

页码：590 / 594

页数：5

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