Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913105875828736 |
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| author | Liu, Yaru Lemos, Dayllon Vinícius Xavier Bozorgian, Ali Zeng, Chengxi Hepburn, Alexander Raventos, Arnau |
| author_facet | Liu, Yaru Lemos, Dayllon Vinícius Xavier Bozorgian, Ali Zeng, Chengxi Hepburn, Alexander Raventos, Arnau |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07959 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering Liu, Yaru Lemos, Dayllon Vinícius Xavier Bozorgian, Ali Zeng, Chengxi Hepburn, Alexander Raventos, Arnau Graphics 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. |
| title | Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering |
| topic | Graphics |
| url | https://arxiv.org/abs/2604.07959 |