# LowPowAR: Power-Constrained Tone Mapping for Augmented Reality

arXiv: https://arxiv.org/abs/2607.19509 · HTML: https://mlsanity.com/paper/2607.19509/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2026.

## Built on by (most cited first, 4 of 4)

- [EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory](https://mlsanity.com/paper/2609.37923/) (2026)
- [ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Rendering](https://mlsanity.com/paper/2609.32038/) (2026)
- [Multidimensional Observer Model and Perceptual Dimensions of Human Image Quality Assessment](https://mlsanity.com/paper/2609.38487/) (2026)
- [GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction](https://mlsanity.com/paper/2609.38519/) (2026)

## Abstract (arXiv)

Everyday-wearable Augmented Reality (AR) glasses must meet strict power limits, making displays a key target for optimization. We cast display power optimization as a power-constrained tone-mapping problem and propose a human-vision-grounded, learning-based framework that maximizes perceptual quality under a given power budget. We introduce an optimization-friendly tone-mapping operator (TMO) parameterization along with a progressive optimization strategy to effectively navigate the quality-vs-power landscape. We distill the iterative optimization into a lightweight feed-forward neural network for real-time deployment. Subjective experiments show that our method yields better perceptual quality than prior work at the same power budget. Project page: https://horizon-lab.org/lowpowar/.