# Compression of phase-only holograms with JPEG standard and deep learning

arXiv: https://arxiv.org/abs/1806.03811 · HTML: https://mlsanity.com/paper/1806.03811/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography](https://mlsanity.com/paper/2310.05299/) (2023)
- [P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms](https://mlsanity.com/paper/2404.01330/) (2024)
- [JPEG-Inspired Cloud-Edge Holography](https://mlsanity.com/paper/2512.12367/) (2025)

## Abstract (arXiv)

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed "JPEG + deep learning" hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.