Compression benchmarking of holotomography data using OME-Zarr format

Abstract

Holotomography (HT) is a label-free, three-dimensional quantitative phase 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 six representative datasets from five biological samples, we evaluated combinations of preprocessing filters and 13 compression algorithms across multiple compression levels. Performance was assessed in terms of compression ratio, bandwidth, and decompression speed. A throughput-based evaluation metric was introduced to capture realistic performance under varying network constraints, revealing that the optimal compression strategy is strongly dependent on available system bandwidth. Across a wide range of bandwidth conditions, Pcodec consistently exhibited the most balanced overall performance, followed by Blosc-zstd and zstd. The results offer practical guidance for the 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.