Better than lossless RAW image compression

7:1 compression ratio
200Mb/s/core processing speed
Embedded noise model
How it worksGet demo
As seen in Scientific Reports
Part of AI standardisation group
En route to satellite
Image quality endorsed by

Metrologically accurate compression combining image quality of lossless compression and file size of lossy formats

Visually lossless
Limited error
Metrologically correct
Loss invisible to the eye
Errors bounded (single-pixel)
Errors are independent
Errors are unbiased
No artefacts
Indistinguishable from raw
Bit accurate
Embedded noise model
Typical compression ratio
Typical compression speeds
Jetraw: any format

"It is not a file format, it is a codec that supports majority of raw formats"

Bruno Sanguinetti, CTO, Dotphoton

Jetraw applies quantum physics insights into raw image compression

"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.”

Chris Dunsby, Imperial College London

Highest compression ratio that simply works in your current setup


200 MB/sec/core processing speed


6 to 10x compression ratio

Plugins and modules

Fiji, LabView, Python, Matlab

Supported formats

TIFF, Big TIFF, OME.TIFF, HDF5, DNG, DICOM (soon), Hyperstack Fiji (soon)

Custom integration

Shared dynamic libraries and header files


AMD/Intel x86-64, Apple M1


Windows 1
MacOS 10.15
Linux with glibc 2.17 or newer (e.g. CentOS 7.6, Ubuntu 13.04)


Conversion gain > 0.3 dn/electron12 to 16 bits per pixel
Monochromatic or Bayer-type color filter array

Supporting foremost life-science cameras and microscopy systems

PCO Edge series PCO Panda series
Hamamatsu Orca Fusion Hamamatsu Orca Fusion BT Hamamatsu Orca Flash 4.0, V3, and V2
Teledyne Photometrics Kinetix Photometrics 95B
Do you use Viventis LS1 Live light sheet microscope system? Benefit from the best possible in-camera compression
Andor Zyla 4.2, 4.2P
Use other cameras?