Assessing Learned Models for Phase-only Hologram Compression

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 [Sitzmann et al. 2020], and FilmSIREN [Chan et al. 2021], with TAESD [Bohan 2023] 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 INR s, SIREN with 4.9k parameters achieves compression with high quality in the reconstructed 3D images (PSNR = 34.54 dB). These results emphasize the effectiveness of INR s and identify the limitations of pretrained image compression VAE s for hologram compression task.