arxiv
PublishedJune 4, 2026 at 4:00 AM
RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
Publisher summary· verbatim
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 mul
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning20harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning20harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models20harxivTemporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents20hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗