Because these detectors are so fast and have no readout noise, fine phi-slicing and high frame rates allow more accurate data, with many pixels having values close to zero. In crystallography, the emergence of hybrid pixel detector technology has led to a significant increase in the amount of data generated per data collection session, producing exponentially growing volumes of diffraction data (Paton et al., 2021 Tate et al., 2016 ). Universal access to exponentially growing data has made efficient data storage and processing crucial for transformative science (Hill et al., 2016 Tolle et al., 2011 ). The C++20 compression/decompression code, custom TIFF library and an ImageJ/ Fiji Java plugin for reading TRPX files are open-sourced on GitHub under the permissive MIT license. By providing a tailored solution for diffraction and raw cryo-EM data, TRPX facilitates more efficient data analysis and interpretation while mitigating storage and transmission concerns. It can therefore be readily implemented in hardware. TRPX files are byte-order independent and upon compilation the algorithm occupies very little memory. It was 60 times faster than bzip2 (which achieved a similar compression rate), and more than 3 times faster than LZ4, which was the runner-up in terms of speed, but had a much worse compression rate. The results show that TRPX significantly outperforms all these algorithms in terms of speed and compression rate. The algorithm is compared with established lossless compression algorithms implemented in gzip, bzip2, CBF (crystallographic binary file), Zstandard( zstd), LZ4 and HDF5 with gzip, LZF and bitshuffle+ LZ4 filters, in terms of compression efficiency and speed, using continuous-rotation electron diffraction data of an inorganic compound and raw cryo-EM data. Here, TERSE/PROLIX (or TRPX for short) is presented, a novel lossless compression algorithm specifically designed for diffraction data. At the moment, the SCIFIO-JavaCV project is inactive due to lack of development resources.High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. The SCIFIO-JavaCV project will offer out-of-the-box support for video formats supported by OpenCV including those supported by FFmpeg. For cases like that, check out media player and the k-lite codec pack. Similarly, for saving video, you can write to an uncompressed format, then compress it afterward using an external tool.įor files larger than 4GB, you may run into trouble with otherwise excellent transcoders like FFmpeg. The uncompressed video stream can then easily be opened in ImageJ without the need for additional plugins. Unfortunately, there is no update site for it you must perform a complex installation procedure manually.Īnother strategy is to transcode your video to an uncompressed format using a tool such as QuickTime Pro, VirtualDub or FFmpeg on the command line. For exporting video, you could try the Save As Movie plugin.The source code for this update site is embedded in the scifio-javacv history. Enable the beta-quality FFMPEG update site, which uses native bindings to the FFmpeg library to read many video formats.Bio-Formats is included with the Fiji distribution of ImageJ. See the Bio-Formats AVI and QuickTime pages for the list of supported codecs. Bio-Formats includes support for reading additional codecs for some video formats.There are several ways to enable support for more video formats: Out of the box, ImageJ has limited support for some video formats such as AVI and QuickTime.
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