Image compression and optimization algorithm allows up to x10 boost of camera performance working in , to make the end-user's life easier working in your current workflow and to deliver images compatible with all existing image processing algorithms and new Artificial Intelligence and Machine Learning algorithms.
VALIDATED BY IMAGING EXPERTS ✔
AI-ready image optimization for medical imaging and microscopy
Boost your performance by
What if you could?
Make sure that images taken today will be compatible with any AI/ML algorithms coming in the next ten years
Divide your storage cost by a factor of 10
Make you image processing and data transfer ten times faster
Lossless or Dotphoton compression
Keep your RAW raw
Long term preservation and compatibility
Keep your usual workflow
Light sheet microscopy
How Dotphoton works
Typical image file formats do not distinguish signal from noise, as this is a challenging task. Dotphoton uses state-of-the-art calibration of the most commonly used imaging devices to ensure that the space in the compressed files is allocated to signal, and not noise.
When compressing an image, Dotphoton find the optimal compression parameters for the specific imaging device in its database, and compresses it, either in our ".p" format (with up to 10:1 compression ratio), or in an existing standard, such as .tiff, .png, .dng etc.
Validated by specialists
We have validated that the results obtained with advanced algorithms on dotphoton-compressed images are statistically equivalent to those obtained from the raw images. This has been done both internally by us, by partners and by customers, with images from a variety of microscopes: bright field, dark field, phase contrast, lightsheet, fluorescence, ...
Quality designed for critical applications
Image and quality
your RAW format open or proprietary
.tiff, .png, .dng, jp2000, .dcm
up to 16 bits, ≥12 bits recommended
no block artefacts, no posterization, no temporal artefacts, no ringing, no contouring, no aliasing, no low-pass filtering
5:1 – 13:1
Typical compression ratio
Min compression ratio
Any system with sCMOS, CCD sensors
.tiff or any lossless open format
Tested on more than 50 cameras from a dozen manufacturers
Dynamically library (dll)
for Windows, Linux and Mac, and has been tested with C, C++, Objective-C, Python, C# and Swift (others may work)
Virtual machine for IT infrastructure
depoyable on vmware to dynamically compress images on network shares
Graphical user interface
for Windows, Linux and Mac
for Windows, Linux and Mac
To validate our compression for applications in microscopy, we partnered with academic institutions, and jointly developed a methodology to test the reliability of compressed data in today's widespread analysis scenarios, as well as testing processing "primitives" to secure compatibility with future applications.
2-D and 3-D segmentation
2-D and 3-D stitching
Four steps to validation
The base algorithm is mathematically analyzed and proven to retain information to within a strict bound
The software/hardware implementation is tested through a series of automated unit, integration and system testing
The full system, consisting of camera->compressor->decompressor is "black box" tested, ensuring that the decompressed pixel value is within the original uncertainty of the camera
Application testing: academic partners compare results of their image processing algorithms on raw and on compressed data, and verify that these are statistically equivalent