With the race towards L4+ vehicles, increased number of sensors per car, resolutions, frame rates, etc., the data produced by the systems grows exponentially. So are the costs.
Thus, efficient and cost-effective data handling becomes crucial. Existing infrastructure cannot cope with the demand.
Traditional lossless compression struggles with scalability due to limited speed and efficiency. While visually lossless algorithms compromise the reliability of ML models for the safety of connected and autonomous vehicle’s (CAV).
Jetraw Core is a high-efficiency low-latency 6:1 raw image compression integrated into certified environments, either on-board a vehicle system or in a data centre.
Jetraw applies lossless compression to the signal and lossy compression to the noise, preserving the metrological properties of raw images while achieving higher compression ratios and speeds than traditional lossless methods.
Unlike visually lossless codecs, Jetraw preserves raw quality and treats images as precise measurements, facilitating reliable data-centric AI analysis and model training.
Developing autonomous vehicles requires massive volumes of image data, and is often hindered by slow upload and download speeds and recurring system capacity constraints. Cloud storage and access costs can easily reach millions.
Jetraw Core overcomes these challenges and enables scalable data infrastructure for OEMs and Tier 1 suppliers.
It's a fair reaction, and one we hear often. The reason Jetraw can deliver compression ratios that other tools can't is fundamentally different from how conventional compressors work.
Most compressors treat an image as an abstract grid of numbers and apply general-purpose mathematical transforms. Jetraw is built around a physical model of the image sensor itself. It understands which variations in pixel values carry real information about the scene and which are noise inherent to the imaging process that can be safely discarded without losing any meaningful signal.
This sensor-aware approach is what makes the difference. By compressing only what is physically meaningful, Jetraw achieves ratios that purely mathematical methods cannot reach, without the trade-offs people typically expect.
Standard codecs are designed for human viewing, not for machine perception or downstream image processing. They discard information that the eye doesn't notice but that your perception stack, calibration tools, or validation pipeline may very much rely on.
Jetraw produces output that is indistinguishable from the original RAW image for all subsequent processing. Whatever your team does with RAW data continues to work exactly as before: neural-network inference stays accurate because the input distribution is preserved, data augmentation produces physically realistic samples because the pixel values are still linear and meaningful, replay and resimulation remain bit-accurate, so regression tests actually test the model rather than codec artifacts, ISP tuning is still possible because the RAW Bayer data is intact and auto-labelling pipelines reach their full accuracy ceiling instead of being capped by lossy inputs.
This matters for two reasons that are especially important in automotive contexts:
Future-proofing. If your data requirements evolve, new perception models, new validation criteria, new regulatory demands, you still have access to the full RAW fidelity. With lossy consumer codecs, that information is gone for good.
Quality guarantees. Jetraw comes with strong, provable bounds on the deviation from the original signal. For safety arguments and certification work, having a mathematically defensible quality statement is far more valuable than "looks fine to the eye." Safety documentation is available through our partners on request.
Jetraw output is genuine RAW image data, indistinguishable from the original for downstream processing, and has been deployed in mission-critical production environments for many years. For automotive specifically, validation tooling, services, and safety documentation are available through our partners, for a fast path to integration and time-to-market.
Getting started is straightforward. We provide a trial version of Jetraw that you can run on your own images and benchmark against your current pipeline.
Because Jetraw relies on a physical model of your specific sensor, we need a little information about it to prepare the trial. Either of the following works:
An EMVA1288 report. if your sensor has been characterised according to this standard, that's all we need.
A set of sample images. if no report is available, we can derive the relevant sensor parameters directly from representative images you provide.
From there, we hand over the trial build and you can evaluate compression ratios, runtime, and output quality on your own hardware.
Yes. Jetraw compresses in real time, with concrete throughput figures from our reference platforms:
Software (CPU): on an Intel i9-14900K running 32 images in parallel across all cores, Jetraw reaches 8 GB/s (4 Gpx/s) compression and 5.6 GB/s (2.8 Gpx/s) decompression. The implementation runs on Intel, AMD, and ARM CPUs across Linux, Windows, and macOS.
FPGA IP core: up to 6.4 Gpx/s compressing 32 pixels in parallel at 200 MHz on Xilinx FPGAs.
For reference, an automotive setup of 5× 2 MP cameras at 30 fps generates ~600 MB/s of RAW data; a heavier configuration with 5× 2 MP + 3× 8 MP cameras at 30 fps generates ~2 GB/s. Jetraw handles both with margin, on either CPU or FPGA.
Compression happens per-frame, so the output integrates cleanly with both streaming and batch-recording workflows.
Compression isn't free, there's always a compute cost, but Jetraw is engineered to keep it modest and predictable.
On CPU, Jetraw is parallelised and scales across cores. On an Intel i9-14900K, compressing 32 images in parallel reaches 4 Gpx/s compression and 2.8 Gpx/s decompression. Jetraw runs on Intel, AMD, and ARM CPUs.
On FPGA, the resource footprint scales with throughput: from 5,534 LUT for a single-pixel-per-clock implementation up to 130,000 LUT for 32 pixels-per-clock at 200 MHz (6.4 Gpx/s).
Not at all. Jetraw is already deployed in production across several demanding industries:
Automotive. for camera-based perception, data-logging fleets, and validation pipelines.
Space. where bandwidth back to Earth is the hardest constraint imaginable.
Microbiology and life sciences. where preserving the integrity of scientific imaging data is non-negotiable.
Each of these domains has strict, independent requirements for image fidelity. Jetraw earned its place in all three.
