Color-Perception-Guided Display Power Reduction for Virtual Reality
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Abstract (arXiv)
Battery life is an increasingly urgent challenge for today's untethered VR and AR devices. However, the power efficiency of head-mounted displays is naturally at odds with growing computational requirements driven by better resolution, refresh rate, and dynamic ranges, all of which reduce the sustained usage time of untethered AR/VR devices. For instance, the Oculus Quest 2, under a fully-charged battery, can sustain only 2 to 3 hours of operation time. Prior display power reduction techniques mostly target smartphone displays. Directly applying smartphone display power reduction techniques, however, degrades the visual perception in AR/VR with noticeable artifacts. For instance, the "power-saving mode" on smartphones uniformly lowers the pixel luminance across the display and, as a result, presents an overall darkened visual perception to users if directly applied to VR content. Our key insight is that VR display power reduction must be cognizant of the gaze-contingent nature of high field-of-view VR displays. To that end, we present a gaze-contingent system that, without degrading luminance, minimizes the display power consumption while preserving high visual fidelity when users actively view immersive video sequences. This is enabled by constructing a gaze-contingent color discrimination model through psychophysical studies, and a display power model (with respect to pixel color) through real-device measurements. Critically, due to the careful design decisions made in constructing the two models, our algorithm is cast as a constrained optimization problem with a closed-form solution, which can be implemented as a real-time, image-space shader. We evaluate our system using a series of psychophysical studies and large-scale analyses on natural images. Experiment results show that our system reduces the display power by as much as 24% with little to no perceptual fidelity degradation.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 2.
7 papers.
- 2023Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challengescited by 12
- 2025Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Servingcited by 2
- 2023AutoColor: Learned Light Power Control for Multi-Color Hologramscited by 1
- 2024Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displayscited by 1
- 2024Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Realitycited by 0
- 2026ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Renderingcited by 0
- 2026GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Predictioncited by 0
- 2026GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction29 Sep 2026
- 2026ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Rendering25 Sep 2026
- 2025Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Serving25 Apr 2025
- 2024Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality4 Aug 2024
- 2024Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displays31 Jul 2024
- 2023Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges23 May 2023
- 2023AutoColor: Learned Light Power Control for Multi-Color Holograms2 May 2023