Multi-Color Compressive Hologram Synthesis with Learned Wave Propagation

Abstract

Holographic displays are a promising technology for delivering immersive, true 3D visualization in virtual and augmented reality applications. However, generating high-fidelity phase-only holograms remains challenging, especially with the demand for efficient compression to handle the substantial data inherent in high-resolution holographic streaming. Existing techniques often struggle to balance the tradeoff between optical display quality and compression efficiency, and jointly optimizing these aspects is still in its infancy. This work presents a learning-empowered multi-color hologram compression scheme that utilizes a pretrained, camera-calibrated wave propagation model, especially for unfiltered holographic display configurations with compact form factors. In particular, the inter-color processing leverages the inherent redundancy across color channels, allowing for efficient compression. By incorporating the learned camera-calibrated wave propagation model into our training process, we can achieve superior optical display quality and compression rates. Experiments demonstrate that our method realizes a reduction in bits per pixel (bpp) of 44% to 74% over representative baselines at the same quality level. We envision the proposed compressive hologram synthesis scheme establishing a new benchmark for high-fidelity holographic reconstruction at lower bitrates, marking a significant advance towards the deployment of holography-empowered visual media systems.