RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912729881640960 |
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| author | Rafiei, Shima Babak, Zahra Nabizadeh Shahr Samavi, Shadrokh Shirani, Shahram |
| author_facet | Rafiei, Shima Babak, Zahra Nabizadeh Shahr Samavi, Shadrokh Shirani, Shahram |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression Rafiei, Shima Babak, Zahra Nabizadeh Shahr Samavi, Shadrokh Shirani, Shahram Machine Learning 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. |
| title | RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.21035 |