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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.

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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