AI RESEARCH
RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
arXiv CS.LG
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ArXi:2511.21035v2 Announce Type: replace 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.