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arXiv · 2507.06646

Assessing Learned Models for Phase-only Hologram Compression

Zicong Peng·Yicheng Zhan·Josef Spjut·Kaan Akşit
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Abstract

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.

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Zicong Peng, Yicheng Zhan, Josef Spjut, Kaan Akşit. 2025-07-24. Assessing Learned Models for Phase-only Hologram Compression. https://doi.org/10.1145/3721250.3742993

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