We evaluate the performance of four common learned models utilizing INR and VAE structures for compressing phase-only holograms in holographic displays. The evaluated models include a vanilla MLP, SIREN, and FilmSIREN, with TAESD as the representative VAE model. Our experiments reveal that a pretrained image VAE, TAESD, with 2.2M parameters struggles with phase-only hologram compression, revealing the need for task-specific adaptations. Among the INRs, SIREN with 4.9k parameters achieves %40 compression with high quality in the reconstructed 3D images (PSNR = 34.54 dB). These results emphasize the effectiveness of INRs and identify the limitations of pretrained image compression VAEs for hologram compression task.
Cite
@article{arxiv.2507.06646,
title = {Assessing Learned Models for Phase-only Hologram Compression},
author = {Zicong Peng and Yicheng Zhan and Josef Spjut and Kaan Akşit},
journal= {arXiv preprint arXiv:2507.06646},
year = {2025}
}