Deep-Learning-Based Energy Aware Images
Olivier Le Meur, Claire-Hélène Demarty, Laurent Blondé
Abstract
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.
Topics & Concepts
Computer scienceComputationEnergy (signal processing)Artificial intelligenceEnergy consumptionReduction (mathematics)Deep learningImage (mathematics)Computer visionAlgorithmMathematicsEngineeringElectrical engineeringGeometryStatisticsImage and Video Quality AssessmentImage Enhancement TechniquesVisual Attention and Saliency Detection