aiqbench analysis
RAVQ-HoloNet introduces a rate-adaptive vector quantization framework that unifies compression and phase-only hologram transformation within a single network architecture. By eliminating the need for multiple distinct models, it achieves significant BD-Rate reductions and PSNR gains over current state-of-the-art methods in low-bit-rate scenarios. This advancement addresses a key bottleneck for holographic AR/VR adoption by improving data efficiency and reconstruction fidelity on both simulated and SLM device benchmarks.
RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
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, outperforming current state-of-the-art methods implemented via two distinct architectural configurations: a standard model optimized for low bit rates and a deeper, extended variant tailored for ultra low bit rate setting. To evaluate these models, we utilized the DIV2K dataset as a benchmark for high-fidelity holographic reconstruction. Quantitative analysis in the simulation reveals that our approach significantly surpasses current benchmarks. Specifically, in the low bit rate domain, our method achieves a BD-Rate reduction of -33.91% and a BD-PSNR gain of 1.02dB relative to the state-of-the-art method. Additionally, experimental results on the SLM device show that our method achieves higher contrast and improved quality.
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, outperforming current state-of-the-art methods implemented via two distinct architectural configurations: a standard model optimized for low bit rates and a deeper, extended variant tailored for ultra low bit rate setting. To evaluate these models, we utilized the DIV2K dataset as a benchmark for high-fidelity holographic reconstruction. Quantitative analysis in the simulation reveals that our approach significantly surpasses current benchmarks. Specifically, in the low bit rate domain, our method achieves a BD-Rate reduction of -33.91% and a BD-PSNR gain of 1.02dB relative to the state-of-the-art method. Additionally, experimental results on the SLM device show that our method achieves higher contrast and improved quality.
Machine Learning (cs.LG)
arXiv:2511.21035 [cs.LG]
(or arXiv:2511.21035v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2511.21035 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 261 words