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Unlock cloud-based raw image data management

Powering top imaging systems & data producers

Data-centric AI is only reliable with high quality raw image data. The downside of raw? Too big to store, transfer and process on the cloud

3PB/year

Projected to be produced by a digital pathology department of one hospital

7.5PB/year

User-level data published from Sentinel satellites, with approximately 230TB of data downloaded daily

80%

time spent by data scientists preparing data rather than creating insights

28.8TB/d

Generated by a test autonomous vehicle

Dotphoton developed raw image compression & data quality validation tools based on sensor modeling for scalable and reliable ML/AI

Errors bounded (single pixel)

Errors independent

No artefacts

Embedded noise model

Compression ratio, avg.

Compression speed, avg.

Lossy
compression

JPEG

7:1

100MB/s
per core

Limited
error

BD3, LERC

5:1

50MB/s
per core

Visually
lossless

JPEG-2000

7:1

10MB/s
per core

Lossless
compression

LZW, FLIF

1.5:1

10MB/s
per core

Metrologically
accurate

Dotphoton

7:1

200MB/s
per core

Instant benefits for every petabyte of your data

Cost of cloud storage
& network, USD/year

1→0.1M

CO2 emissions,
tonnes/year

52→6t

Time required
to transfer on 100Gbps

22→2.8h

Tools based on sensor noise model to improve machine learning & MLOps

Synthetic data generation

Reliably emulate different acquisition modalities (less intensity, higher exposure, etc) in real-life environments, by generating images that follow the same distribution albeit with statistically correct variations.

Dataset quality assurance

Prevent systematic bias in data distribution that degrades ML models, as Dotphoton adds robustness to data changes in illumination, magnification, and other sensor parameters.

Data traceability

Track images based on the generation of the metadata to improve data accessibility.

Instant benefits for every petabyte of your data

Faster cloud processing

Cloud-based image handling at low latency for more reliable and scalable data-centric AI for connected and autonomous vehicles

business

Identify out-of-distribution data

Discover ML algorithm tolerances to instrument & environmental changes, e.g. resolution, temperature, lighting, focus, contrast

ML

84%

less energy used with networking & storage optimization

business

Normalize data to one virtual sensor

Higher reliability & lower latency from a more compact model by normalising data

ML

6:1

typical compression ratio

business

Higher resolution & better AI

Add quality validation without increasing hardware costs related to RAM, storage and in-car communications

ML

Higher throughput

over light and affordable cable connections

ML

8:1

typical
compression
ratio

business

Retain & reuse more data for legal & publication requirements and increase research efficiency

business

More resources for research

Free up time by transmitting petabytes of image data 8x faster on cloud or local storage

business

Less CO2 emissions

Reduce carbon footprint 8x by lowering storage needs and energy consumption with smaller data

business

Equally good for human and machine vision

Ensure the metrological quality of images based on our embedded noise model & sensor data

ML

Laboratory -> Clinical

Establish requirements & tolerances moving from research to production instruments

business

Increase model generalisation

by propagating errors with Monte-Carlo simulations on synthetic data emulating different acquisition modalities

ML

Reduce prototyping

Discover how satellite optical & sensor parameters affect AI/ML performance models to enhance their robustness

ML

Increase satellite sensor resolution & frame rate

Work with higher quality images applying ML super-resolution and denoising

ML

4x

more images stored & processed on the onboard RAM and flash

business

4:1

typical compression ratio

business

Download x4 more images on the same downlink

business

Lower costs & CO2 emissions

Distribute and store earth observation image data on ground faster, more affordably and environmentally-efficient

business

Generate synthetic image data from auto-labelled aerial & drone images to achieve ML results with better-than-human performance

ML

5:1

more images on cloud without plan upgrade

Keep memories, not the CO2

Access/store image data more affordably & environmentally-efficient

55+ cameras supported

8:1

compression ratio
in consumer cameras

No processing limitations

No dynamic range change

No color loss

No bit count change

Save hundreds on new HDD/SSD and NAS upgrades

Products to reduce storage costs and enable on-cloud processing

Jetraw Core — Fast & power-efficient FPGA compression for camera manufacturers

Flexible FGPA IP Core or software integration
200MB/s speed
No artifacts, no bias, no filtering

Jetraw — Compression software for research & industry

Embedded noise model
No artifacts, no bias, no filtering
Free decoding

“With the world relying on remote sensing satellite data, there’s a paradigm shift from ‘raw data delivery’ to ‘information delivery’. But those daily petabytes come at a high cost.

Dotphoton preserves the image quality of our data while attaining high compression ratios, which was only possible with high information loss in the past. This allows storing full information, and processing it faster”

Roberto Camarero,
Onboard Payload Data Processing Engineer European Space Agency
Meet Jetraw onboarding team to start your discovery workshop and calculate benefits

Research & insights

Good practices for health applications of machine learning: Considerations for manufacturers and regulators

Available from ITU website, 2023

Physical data models in machine learning imaging pipelines

Machine Learning and the Physical Sciences workshop, NeurIPS 2022, selected for a contributed talk

Data models for dataset drift controls in machine learning with optical images

TMLR (Transactions on Machine Learning Research), 2023. Presented as workshop paper at: ICML Spurious Correlations, Invariance, and Stability Workshop, 2023 • ICML Differentiable Almost Everything Workshop, 2023

Data-centric AI workflow based on compressed raw images

8th International Workshop on On-Board Payload, Athens, 26 September 2022

Meet our team of scientists & imaging experts

Integration type
FPGA IP Core
High throughput, low latency, power-efficient raw compression on FPGA
Software
Fast, easily integrated in-camera raw compression as a software
5–10:1 compression ratio
Indistinguishable from raw, interoperable
CMOS, sCMOS, CCD camera support
Mono, Bayer and other CFA image sensors support
No bias, no artifacts, no artificial correlations, no low pass filtering
Tightly-controlled image quality 1.2dB SNR equiv. increase ISO100→ISO115
Image data
  • 16-bit images
  • Configurable image dimensions
Raw image buffer or common file formats
Performance
  • 1 to 32 pixels per clock cycle
  • Up to 200 MHz clock frequency
  • Low latency (~1-line, 2-lines for Bayer)
  • 200MB /s/core
  • Multi-threading support
Integration features
  • Backpressure support
  • This is some text inside of a div block.
  • From 3968 LUT for single core compressor to 70790 for 32 pixels
  • AXI4-stream data interface
  • Available as a software library / codec
  • Callable from C, C++, C#, Java, Python
System support
  • Xilinx FPGAs
  • Intel on request
  • Intel, AMD and ARM CPU support
  • Linux, Windows and Mac support