"We apply full information-theoretical model of the image acquisition process, based on the quantum properties of light and image sensor characteristics to achieve the highest compression ratio on the market."
Christoph Clausen, Chief Scientist, Dotphoton
The three pillars of our compression are image sensor characterisation and modelling, accurate noise replacement, and metrological tests.
Most of the entropy of a given pixel value can be attributed to noise, namely about 9 bpp on a well-exposed 16 bpp sensor, and only about 1 bpp is actual information (signal). Signal and noise are mixed in a complex way, it’s impossible to deterministically distinguish them, unless one knows the signal.
Jetraw technology is based on ‘untangling’ information from noise by calibrating the sensor, thus enabling the high compression ratio. ‘Untangling’ cannot be done fully, as Jetraw is still bound by the rules of information theory. Reduction of signal-to-noise (SNR) is kept at a minimum by enforcing strictly bounded, uniform, unbiased and uncorrelated errors.
“We selected the Jetraw by Dotphoton due to the combination of the method’s tight control on the maximum compression error, the compression ratio achieved and the algorithm speed. In the image acquisition pipeline for our oblique plane light sheet fluorescence microscope we achieve a compression factor of about 7-fold, which provides a big reduction in data storage costs.”
200 MB/sec/core processing speed
6 to 10x compression ratio
Fiji, LabView, Python, Matlab
TIFF, Big TIFF, OME.TIFF, HDF5, DNG, DICOM (soon), Hyperstack Fiji (soon)
Shared dynamic libraries and header files
AMD/Intel x86-64, Apple M1
Windows 11
MacOS 10.15
Linux with glibc 2.17 or newer (e.g. CentOS 7.6, Ubuntu 13.04)
CMOS or CCD
Conversion gain > 0.3 dn/electron12 to 16 bits per pixel
Monochromatic or Bayer-type color filter array
