LowPowAR：用于增强现实的功率受限色调映射
LowPowAR: Power-Constrained Tone Mapping for Augmented Reality
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中文总结 AI 辅助
研究针对日常可穿戴AR眼镜功率受限问题，提出基于人类视觉和学习的框架，通过优化友好的TMO参数化及渐进策略，将迭代优化转化为轻量级神经网络实时部署，在相同功率预算下提升了感知质量。
中文摘要 AI 辅助
日常可穿戴的增强现实（AR）眼镜必须满足严格的功率限制，这使得显示器成为优化的关键目标。我们将显示器功率优化视为功率受限的色调映射问题，并提出了一个基于人类视觉和学习的框架，该框架在给定功率预算下最大化感知质量。我们引入了优化友好的色调映射算子（TMO）参数化以及渐进优化策略，以有效探索质量与功率的关系。我们将迭代优化提炼为轻量级前馈神经网络以进行实时部署。主观实验表明，在相同功率预算下，我们的方法比先前工作具有更好的感知质量。
英文摘要
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/.
