Towards Edge Holography via Implicit Neural Representation and Compression

Holographic displays offer the promise of realistic 3D visualization for virtual and augmented wearable solutions. Nevertheless, existing computer-generated holography (CGH) methods often struggle with either a high computational burden or limited display realism. While the emerging cloud-edge computing mechanism can enable the real-time streaming of holograms, classic image compression techniques struggle to efficiently encode and decode the substantial high-frequency information inherent in hologram data. In light of these challenges, we present a display-aware and lightweight CGH framework, leveraging implicit neural representations (INRs) and camera-calibrated wave propagation, to generate and compress high-fidelity phase-only holograms. Specifically, our approach interprets hologram generation as a continuous function approximation problem, enabling the network, with reduced parameters, to effectively learn the inherent periodicity and high-frequency components of 2D and 3D hologram data. To enable efficient deployment, we further incorporate quantization-aware training, followed by entropy coding. Experimental results evaluated on an unfiltered holographic display prototype demonstrate that the proposed INR-CGH retains image quality comparable to that of existing optimization-based methods in both 2D and 3D scenarios. In addition, our compact INR representation achieves up to 11× compression rate with minimal quality degradation and can be further reduced via quantization-aware training. The resulting model enables ≥250 fps in decoding speed, paving the way towards edge holography.