Data Compression in Transmission Electron Microscopy

ORAL

Abstract

Four-dimensional scanning transmission electron microscopy (4D STEM) and cryo-electron microscopy (cryo-EM) have revolutionized the fields of structural biology, nanotechnology, and material science. However, the vast amounts of data generation poses significant challenges for data storage, data transfer, and analysis. As the size and complexity of microscope datasets continue to grow, efficient data compression techniques are necessary. Effective data compression can significantly reduce the storage requirements and facilitate the sharing and analysis of large datasets. This study investigates the performance of various compression algorithms for microscopy data. We evaluate each algorithm using a diverse set of microscopy datasets.

*Data was acquired at the Electron Imaging Center for Nanosystems (EICN) at the University of California, Los Angeles's California for NanoSystems Institute (CNSI). This work was supported by the BioPACIFIC Materials Innovation Platform of the National Science Foundation under Award No. DMR-1933487.

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Presenters

  • James Done

    • University of California Los Angeles

Authors

  • James Done

    • University of California Los Angeles

  • Ambarneil Saha

    • LBNL
    • Lawrence Berkeley National Laboratory

  • Jungyoun Cho

    • University of California Los Angeles
    • University of California, Los Angeles
    • California NanoSystems Institute (CNSI), University of California, Los Angeles, California 90095, USA
    • California NanoSystems Institute (CNSI), University of California

  • Shervin Nia

    • University of California Los Angeles

  • David Strugatsky

    • University of California Los Angeles

  • Lucas Lee

    • University of California Los Angeles

  • Peter Ercius

    • LBNL
    • Lawrence Berkeley National Laboratory
    • Lawrence Berkeley national laboratory

  • Matthew H Mecklenburg

    • University of California Los Angeles