Beamr's CABR technology achieves 48% file size reduction with minimal impact on ML model accuracy, showcased at CES 2026.
Quiver AI Summary
Beamr Imaging Ltd. announced significant benchmark testing results showing that its Content-Adaptive Bitrate (CABR) technology can reduce storage requirements for autonomous vehicle (AV) video data by up to 50% while maintaining nearly the same accuracy for machine learning models. This testing addresses the challenges of managing the vast amounts of video data generated by AV systems, which can cost a lot in terms of budget and infrastructure. During tests using the PandaSet dataset, CABR technology demonstrated an average file size reduction of approximately 48% with less than a 2% difference in mean Average Precision for object detection, a key performance metric for AV systems. Beamr will showcase these results and discuss ML-safe AV pipelines at CES 2026, encouraging interested teams to meet with their experts during the event.
Potential Positives
- Benchmark testing confirms that Beamr's Content-Adaptive Bitrate (CABR) technology can achieve up to 50% storage reduction for autonomous vehicle video data while maintaining model accuracy with less than 2% impact on machine learning performance.
- The results are validated through an analysis using a credible multi-camera AV dataset, enhancing Beamr's reputation in the industry.
- Meeting opportunities at CES 2026 provide a platform for Beamr to connect with key industry players and showcase their technology advancements directly to AV teams and developers.
Potential Negatives
- Forward-looking statements in the press release highlight significant uncertainties and risks related to Beamr's strategic plans and future performance, which may concern investors.
- The reliance on the CABR technology's performance in maintaining ML accuracy with less than 2% difference raises questions about data integrity and robustness, potentially impacting its adoption in critical autonomous vehicle applications.
- The emphasis on a benchmarking study may suggest that Beamr is under pressure to validate its technology in a competitive market, indicating it may not have fully penetrated or secured its desired market share.
FAQ
What is Beamr's latest achievement in video compression technology?
Beamr's testing demonstrates up to 48% file size reduction while maintaining robust ML model accuracy through their CABR technology.
Where can AV teams meet Beamr at CES 2026?
AV teams can meet Beamr at CES 2026 in Las Vegas from January 6-9, 2026.
How does CABR technology benefit autonomous vehicles?
CABR technology reduces video data storage needs by up to 50% without compromising ML model performance.
What industry-standard metrics were used in Beamr's testing?
The testing utilized metrics such as mean Average Precision (mAP), PSNR, and LPIPS for evaluating model accuracy.
How can I schedule a meeting with Beamr at CES 2026?
You can schedule a meeting with Beamr by visiting the link provided in their press release.
Disclaimer: This is an AI-generated summary of a press release distributed by GlobeNewswire. The model used to summarize this release may make mistakes. See the full release here.
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Full Release
Testing demonstrates 48% file size reduction with robust ML model accuracy across multiple industry-standard metrics. AV teams are invited to meet Beamr at CES 2026, January 6-9 in Las Vegas
Herzliya, Israel, Dec. 22, 2025 (GLOBE NEWSWIRE) -- Beamr Imaging Ltd. (NASDAQ: BMR), a leader in video optimization technology and solutions, published benchmark testing results validating that its patented Content-Adaptive Bitrate (CABR) technology delivers up to 50% storage reduction for autonomous vehicle (AV) video data with comprehensive technical demonstration of machine learning (ML) model accuracy.
AV teams and developers attending CES 2026 in Las Vegas, from January 6-9, 2026, are invited to schedule a meeting with Beamr’s video data experts to review the results and discuss ML-safe AV pipelines. To schedule a meeting at CES 2026, visit this link .
The new testing addresses a critical challenge in AV development: balancing the need to reduce massive amounts of real-world and synthetic video data while maintaining the fidelity required for accurate ML model performance. AV systems produce hundreds of petabytes of multi-camera footage, creating substantial costs in budget and infrastructure.
"AV teams need proven approaches to manage video data at scale without risking their ML pipeline integrity,” said Beamr Chief Product Officer, Dani Megrelishvili. “The benchmark testing validates that transformation is possible with confidence - less than 2% model accuracy impact while achieving significant compression efficiency."
The benchmark testing compared Beamr’s CABR technology against industry-standard workflows using PandaSet, a real-world, multi-camera AV dataset. The validation focused on object detection, a foundational task for AV perception systems, deploying a YOLOv8 (Nano) model on both baseline and CABR-compressed videos. The analysis measured model accuracy across the most prevalent classes in the AV industry: persons, cars, motorcycles, and trucks.
Results showed CABR achieved approximately 48% average file size reduction with less than 2% difference in mean Average Precision (mAP), a standard metric for object detection reliability. The testing confirmed robust results across multiple other industry-standard quality metrics, such as PSNR and LPIPS.
The complete benchmark testing methodology and results are available in Beamr's blog post .
To schedule a meeting at CES 2026, visit this link .
About Beamr
Beamr (Nasdaq: BMR) is a world leader in content-adaptive video compression, trusted by top media companies including Netflix and Paramount. Beamr’s perceptual optimization technology (CABR) is backed by 53 patents and a winner of Emmy® Award for Technology and Engineering. The innovative technology reduces video file sizes by up to 50% while preserving quality and enabling AI-powered enhancements.
Beamr powers efficient video workflows across high-growth markets, such as media and entertainment, user-generated content, machine learning, and autonomous vehicles. Its flexible deployment options include on-premises, private or public cloud, with convenient availability for Amazon Web Services (AWS) and Oracle Cloud Infrastructure (OCI) customers.
For more details, please visit www.beamr.com or the investors’ website www.investors.beamr.com
Forward-Looking Statements
This press release contains “forward-looking statements” that are subject to substantial risks and uncertainties. Forward-looking statements in this communication may include, among other things, statements about Beamr’s strategic and business plans, technology, relationships, objectives and expectations for its business, the impact of trends on and interest in its business, intellectual property or product and its future results, operations and financial performance and condition. All statements, other than statements of historical fact, contained in this press release are forward-looking statements. Forward-looking statements contained in this press release may be identified by the use of words such as “anticipate,” “believe,” “contemplate,” “could,” “estimate,” “expect,” “intend,” “seek,” “may,” “might,” “plan,” “potential,” “predict,” “project,” “target,” “aim,” “should,” “will” “would,” or the negative of these words or other similar expressions, although not all forward-looking statements contain these words. Forward-looking statements are based on the Company’s current expectations and are subject to inherent uncertainties, risks and assumptions that are difficult to predict. Further, certain forward-looking statements are based on assumptions as to future events that may not prove to be accurate. For a more detailed description of the risks and uncertainties affecting the Company, reference is made to the Company’s reports filed from time to time with the Securities and Exchange Commission (“SEC”), including, but not limited to, the risks detailed in the Company’s annual report filed with the SEC on March 4, 2025 and in subsequent filings with the SEC. Forward-looking statements contained in this announcement are made as of the date hereof and the Company undertakes no duty to update such information except as required under applicable law.
Investor Contact:
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