提出可自适应码率的全息图压缩框架，提升画质并节省带宽。
RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
- 基于向量量化设计自适应码率压缩架构，统一模型覆盖不同带宽需求。
- 低码率下比当前最佳方法节省33.91%码率，峰值信噪比提升1.02dB。
- 适用于AR/VR等对全息图传输有高画质要求的应用场景。
全息技术在增强现实与虚拟现实应用中具有巨大潜力，但其推广受限于数据压缩的高需求。现有深度学习方法通常缺乏单一网络内的码率自适应能力，且常需多个模型来满足不同带宽要求。本文提出RAVQ-HoloNet，一种将码率自适应压缩与图像转相位全息图变换相结合的向量量化框架。该框架通过两种不同结构实现高保真重建：标准模型优化低码率场景，深层扩展版本专用于超低码率设置。以DIV2K数据集为基准评估，仿真结果表明，本方法显著超越现有基准。在低码率域，相比当前最优方法，本方案实现BD-Rate降低33.91%，BD-PSNR提升1.02dB。此外，基于SLM设备的实验显示，本方法具备更高对比度和更优视觉质量。
原文摘要 · Abstract (English)
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.
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