As autonomous driving advances and integrates with Physical AI applications, the training and validation pipelines performance determine how far the model development can advance. Perception, validation, and simulation engineers need a proven compression approach that will support these scalable pipelines, often reaching hundreds of petabytes.
Beamr is offering its ML-safe video data stack, enabling up to 50% file size reduction without degrading model performance. The stack was validated from perception models through to world foundation models, on real-world and synthetic footage.
Recently, Beamr demonstrated that video compressed with its patented content-adaptive bitrate (CABR) technology can be used as an augmentation step in AI model training. The study demonstrated that treating compression as a training strategy allows it to scale efficiently while preserving the perception accuracy ML systems depend on. A state-of-the-art depth model trained with Beamr-compressed footage, delivered a 30.7% reduction in depth error on vulnerable road users (VRUs), including pedestrians and motorcyclists, while compressing the training data by 35.2% compared to baseline.
BMR shares began Wednesday up three cents, or 1%, to $3.19.
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