{"ID":2836219,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21035","arxiv_id":"2511.21035","title":"RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression","abstract":"Holography offers significant potential for AR/VR applications, yet its adoption is limited by the high demands of data compression. Existing deep learning approaches generally lack rate adaptivity within a single network. We present RAVQ-HoloNet, a rate-adaptive vector quantization framework that achieves high-fidelity reconstructions at low and ultra-low bit rates, outperforming current state-of-the-art methods. In low bit, our method exceeds by -33.91% in BD-Rate and achieves a BD-PSNR of 1.02 dB from the best existing method demonstrated by the rate-distortion curve.","short_abstract":"Holography offers significant potential for AR/VR applications, yet its adoption is limited by the high demands of data compression. Existing deep learning approaches generally lack rate adaptivity within a single network. We present RAVQ-HoloNet, a rate-adaptive vector quantization framework that achieves high-fidelit...","url_abs":"https://arxiv.org/abs/2511.21035","url_pdf":"https://arxiv.org/pdf/2511.21035v1","authors":"[\"Shima Rafiei\",\"Zahra Nabizadeh Shahr Babak\",\"Shadrokh Samavi\",\"Shahram Shirani\"]","published":"2025-11-26T04:13:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}