arXiv:2511.21035v2 Announce Type: replace Abstract: Holography offers significant potential for AR/VR applications. However, its adoption is limited by the high demand for data compression. Existing deep learning approaches generally lack rate adaptivity within a single network and often require multiple models to cover different bandwidth requirements. We present RAVQ-HoloNet, a rate-adaptive vector quantization framework that integrates the rate-adaptive compression with the transformation of image data into phase-only hologram. RAVQ-HoloNet achieves high-fidelity reconstructions, outperform
The increasing demand for immersive AR/VR experiences, coupled with the computational and bandwidth constraints of current holography, makes efficient compression a critical bottleneck, driving innovation in this area.
This development addresses a key technical limitation in the adoption of holographic technology, crucial for advancing AR/VR and other applications, potentially making high-fidelity immersive experiences more accessible.
The ability to dynamically compress holographic data within a single network, rather than requiring multiple models, simplifies development and deployment, making holographic AR/VR more viable for practical use cases.
- · AR/VR hardware manufacturers
- · Metaverse platforms
- · Digital content creators
- · Telecommunications companies
- · Traditional fixed-rate compression algorithms
- · Less efficient holographic display technologies
Holographic display integration into consumer devices becomes more feasible and cost-effective due to reduced data transfer requirements.
New immersive applications and services emerge, leveraging high-fidelity, rate-adaptive holography for remote work, entertainment, and education.
The widespread adoption of holographic interfaces fundamentally alters human-computer interaction paradigms, moving beyond flat screens to volumetric data interaction.
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