@article{Ban2026, 
author = {Hyunmin Ban and Wenbin Zhou and Xiangyu Meng and Yifan (Evan) Peng},
title = {Multi-color compressive hologram synthesis with learned wave propagation},
year = {2026},
journal = {Computational Visual Media},
volume = {12},
number = {2},
pages = {435-448},
keywords = {computer-generated hologram (CGH), image compression, learned propagation, virtual reality (VR), augmented reality (AR)},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450497},
doi = {10.26599/CVM.2025.9450497},
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 trade-off 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 pre-trained, 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.}
}