Recent advances in computer-generated holography (CGH) have significantly improved visual quality through high-resolution rendering; however, the accompanying increase in data size has become a critical obstacle to practical deployment. Conventional image compression techniques such as JPEG and high efficiency video coding (HEVC) do not adequately account for the unique statistical and spectral characteristics of holograms, thereby limiting both compression efficiency and reconstruction quality. This study proposes an efficient compression framework specialized for digital holograms by integrating a ringing reduction technique for diffraction calculations with CompressAI, a deep learning-based image compression framework. We demonstrate that ringing artifacts are a major factor hindering efficient hologram compression, and show that neural networks can achieve superior performance by learning the intrinsic statistical and spectral features of holograms. Our method achieves a superior balance between compression efficiency and reconstruction quality compared to conventional approaches, particularly at low bit rates. Furthermore, by introducing a multi-channel input representation, our method achieves higher compression ratios.
Sekiguchi, M, Huang, C-H, Blinder, D, Yabuki, S, Wang, F, Schelkens, P, Tu, H-Y, Cheng, C-J, Ito, T & Shimobaba, T 2026, 'Deep learning-based complex hologram compression enhanced by ringing reduction', Optics Continuum, vol. 5, no. 1, 581618, pp. 170-186. https://doi.org/10.1364/OPTCON.581618
Sekiguchi, M., Huang, C.-H., Blinder, D., Yabuki, S., Wang, F., Schelkens, P., Tu, H.-Y., Cheng, C.-J., Ito, T., & Shimobaba, T. (2026). Deep learning-based complex hologram compression enhanced by ringing reduction. Optics Continuum, 5(1), 170-186. Article 581618. https://doi.org/10.1364/OPTCON.581618
@article{63df146ca699449f9282cad78995d64f,
title = "Deep learning-based complex hologram compression enhanced by ringing reduction",
abstract = "Recent advances in computer-generated holography (CGH) have significantly improved visual quality through high-resolution rendering; however, the accompanying increase in data size has become a critical obstacle to practical deployment. Conventional image compression techniques such as JPEG and high efficiency video coding (HEVC) do not adequately account for the unique statistical and spectral characteristics of holograms, thereby limiting both compression efficiency and reconstruction quality. This study proposes an efficient compression framework specialized for digital holograms by integrating a ringing reduction technique for diffraction calculations with CompressAI, a deep learning-based image compression framework. We demonstrate that ringing artifacts are a major factor hindering efficient hologram compression, and show that neural networks can achieve superior performance by learning the intrinsic statistical and spectral features of holograms. Our method achieves a superior balance between compression efficiency and reconstruction quality compared to conventional approaches, particularly at low bit rates. Furthermore, by introducing a multi-channel input representation, our method achieves higher compression ratios.",
keywords = "Biomedical imaging, Holographic displays, Holographic techniques, Imaging techniques, Neural networks, Speckle noise",
author = "Makoto Sekiguchi and Chung-Hsuan Huang and David Blinder and Shosei Yabuki and Fan Wang and Peter Schelkens and Han-Yen Tu and Chau-Jern Cheng and Tomoyoshi Ito and Tomoyoshi Shimobaba",
year = "2026",
month = jan,
day = "12",
doi = "10.1364/OPTCON.581618",
language = "English",
volume = "5",
pages = "170--186",
journal = "Optics Continuum",
issn = "2770-0208",
publisher = " [Washington DC]: Optica Publishing Group, 2022-",
number = "1",
}