Many client-side applications, especially games, render video at high resolution and frame rate on power-constrained devices, even when users perceive little or no benefit from all those extra pixels. Existing perceptual video quality metrics can indicate when a lower resolution is "good enough", but they are full-reference and computationally expensive, making them impractical for real-world applications and deployment on-device. In this work, we leverage the spatio-temporal limits of the human visual system and propose a non-reference method that predicts, from the rendered video alone, the lowest resolution that remains perceptually indistinguishable from the best available option, enabling power-efficient client-side rendering. Our approach is codec-agnostic and requires only minimal modifications to existing infrastructure. The network is trained on a large dataset of rendered content labeled with a full-reference perceptual video quality metric. The prediction significantly enhances perceptual quality while substantially reducing computational costs, suggesting a practical path toward perception-guided, power-efficient client-side rendering.
@article{arxiv.2604.07959,
title = {Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering},
author = {Yaru Liu and Dayllon Vinícius Xavier Lemos and Ali Bozorgian and Chengxi Zeng and Alexander Hepburn and Arnau Raventos},
journal= {arXiv preprint arXiv:2604.07959},
year = {2026}
}
Comments
Withdrawn to complete standard internal institutional regulatory clearance processes prior to publication.