Compression benchmarking of holotomography data using the OME-Zarr storage format
Abstract
Holotomography (HT) is a label-free, three-dimensional imaging technique that captures refractive index distributions of biological samples at sub-micron resolution. As modern HT systems enable high-throughput and large-scale acquisition, they produce terabyte-scale datasets that require efficient data management. This study presents a systematic benchmarking of data compression strategies for HT data stored in the OME-Zarr format, a cloud-compatible, chunked data structure suitable for scalable imaging workflows. Using representative datasets-including embryo, tissue, and birefringent tissue volumes-we evaluated combinations of preprocessing filters and 25 compression configurations across multiple compression levels. Performance was assessed in terms of compression ratio, bandwidth, and decompression speed. A throughput-based evaluation metric was introduced to simulate real-world conditions under varying network constraints, supporting optimal compressor selection based on system bandwidth. The results offer practical guidance for storage and transmission of large HT datasets and serve as a reference for implementing scalable, FAIR-aligned imaging workflows in cloud and high-performance computing environments.
Keywords
Cite
@article{arxiv.2503.18037,
title = {Compression benchmarking of holotomography data using the OME-Zarr storage format},
author = {Dohyeon Lee and Juyeon Park and Juheon Lee and Chungha Lee and YongKeun Park},
journal= {arXiv preprint arXiv:2503.18037},
year = {2025}
}