Beamr Imaging stock surges on autonomous vehicle compression solution

Published 11/06/2025, 16:14
© Reuters

Investing.com -- Beamr Imaging Ltd (NASDAQ:BMR) stock jumped 16% after the company announced plans to launch a high-performance video compression solution designed specifically for autonomous vehicles at NVIDIA (NASDAQ:NVDA) GTC Paris.

The video optimization technology provider revealed its Content-Adaptive Bitrate (CABR) technology can reduce autonomous driving and synthetic video file sizes by up to 50% while maintaining visual quality and preserving critical features needed for training autonomous driving models. The solution addresses a significant challenge in the autonomous vehicle industry, where a single vehicle can produce terabytes of video data daily.

Built on NVIDIA accelerated computing, Beamr’s technology aims to tackle the mounting storage demands and infrastructure costs facing autonomous vehicle companies. According to the company, training a single autonomous model may require tens to hundreds of petabytes of data, creating substantial operational expenses.

"Autonomous vehicle companies are under mounting pressure from rising video storage demands and infrastructure costs," said Sharon Carmel, Beamr CEO. "Our content-adaptive technology, accelerated by GPUs, delivers highly efficient compression while maintaining visual quality across a variety of scenarios - both for human perception and machine vision, and in both real-world and synthetic video."

The company reported that recent benchmark testing on raw driving footage using real-time object detection models demonstrated compression rates equivalent to the highest-quality compression common in the industry while maintaining high detection accuracy and showing virtually no impact on machine learning performance.

Beamr’s technology is designed to work with both real-world driving footage and synthetic video generated by platforms such as NVIDIA Omniverse and NVIDIA Cosmos world foundation models, which are essential for training autonomous driving systems across various scenarios.

This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.

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