Beamr Reports Entering PoCs in Video Data Compression Solution for Autonomous Vehicle
Beamr Imaging Ltd. (NASDAQ: BMR) announced progress in validating its video optimization technology for autonomous vehicles (AV). The company has successfully conducted multiple Proof of Concepts (PoCs) with AV system developers, demonstrating that its Content-Adaptive Bitrate (CABR) technology can achieve 20%-50% savings on video data used in ML model training without compromising results.
The technology maintains both human-perceptible visual quality and machine learning model stability. This is particularly significant as video is the primary data type in autonomous driving development, with a single vehicle generating terabytes of data daily and model training requiring up to hundreds of petabytes of data.
Beamr Imaging Ltd. (NASDAQ: BMR) ha annunciato progressi nella validazione della sua tecnologia di ottimizzazione video per veicoli autonomi (AV). L'azienda ha condotto con successo diversi Proof of Concept (PoC) con sviluppatori di sistemi AV, dimostrando che la sua tecnologia Content-Adaptive Bitrate (CABR) può ottenere un risparmio del 20%-50% sui dati video utilizzati per l'addestramento dei modelli di machine learning senza compromettere i risultati.
La tecnologia preserva sia la qualità visiva percepibile dall'uomo sia la stabilità dei modelli di machine learning. Ciò è particolarmente rilevante poiché il video rappresenta il tipo di dato principale nello sviluppo della guida autonoma, con un singolo veicolo che genera terabyte di dati ogni giorno e l'addestramento dei modelli che richiede fino a centinaia di petabyte di dati.
Beamr Imaging Ltd. (NASDAQ: BMR) anunció avances en la validación de su tecnología de optimización de video para vehículos autónomos (AV). La empresa ha realizado con éxito múltiples Pruebas de Concepto (PoCs) con desarrolladores de sistemas AV, demostrando que su tecnología Content-Adaptive Bitrate (CABR) puede lograr un ahorro del 20%-50% en los datos de video utilizados para el entrenamiento de modelos de aprendizaje automático sin comprometer los resultados.
La tecnología mantiene tanto la calidad visual perceptible para humanos como la estabilidad del modelo de aprendizaje automático. Esto es especialmente significativo ya que el video es el tipo de dato principal en el desarrollo de la conducción autónoma, con un solo vehículo generando terabytes de datos diariamente y el entrenamiento de modelos requiriendo hasta cientos de petabytes de datos.
Beamr Imaging Ltd. (NASDAQ: BMR)는 자율주행차(AV)를 위한 비디오 최적화 기술 검증에서 진전을 발표했습니다. 회사는 AV 시스템 개발자들과 여러 차례 개념 증명(PoC)을 성공적으로 수행하며, 자사의 Content-Adaptive Bitrate(CABR) 기술이 머신러닝 모델 훈련에 사용되는 비디오 데이터에서 20%-50% 절감을 달성하면서도 결과에 영향을 주지 않는다는 것을 입증했습니다.
이 기술은 인간이 인지할 수 있는 시각적 품질과 머신러닝 모델의 안정성을 모두 유지합니다. 이는 자율주행 개발에서 비디오가 주요 데이터 유형이며, 단일 차량이 매일 테라바이트 단위의 데이터를 생성하고 모델 훈련에는 수백 페타바이트에 달하는 데이터가 필요하다는 점에서 특히 중요합니다.
Beamr Imaging Ltd. (NASDAQ : BMR) a annoncé des progrès dans la validation de sa technologie d'optimisation vidéo pour les véhicules autonomes (AV). L'entreprise a mené avec succès plusieurs preuves de concept (PoC) avec des développeurs de systèmes AV, démontrant que sa technologie Content-Adaptive Bitrate (CABR) peut permettre des économies de 20 % à 50 % sur les données vidéo utilisées pour l'entraînement des modèles d'apprentissage automatique sans compromettre les résultats.
La technologie préserve à la fois la qualité visuelle perceptible par l'humain et la stabilité des modèles d'apprentissage automatique. Cela est particulièrement important car la vidéo est le type principal de données dans le développement de la conduite autonome, un seul véhicule générant des téraoctets de données chaque jour et l'entraînement des modèles nécessitant jusqu'à des centaines de pétaoctets de données.
Beamr Imaging Ltd. (NASDAQ: BMR) gab Fortschritte bei der Validierung seiner Videooptimierungstechnologie für autonome Fahrzeuge (AV) bekannt. Das Unternehmen hat erfolgreich mehrere Proof of Concepts (PoCs) mit Entwicklern von AV-Systemen durchgeführt und dabei gezeigt, dass seine Content-Adaptive Bitrate (CABR)-Technologie 20%-50% Einsparungen bei den Videodaten für das Training von ML-Modellen erzielen kann, ohne die Ergebnisse zu beeinträchtigen.
Die Technologie erhält sowohl die für Menschen wahrnehmbare visuelle Qualität als auch die Stabilität der Machine-Learning-Modelle. Dies ist besonders bedeutsam, da Video der primäre Datentyp in der Entwicklung autonomer Fahrzeugsysteme ist, wobei ein einzelnes Fahrzeug täglich Terabytes an Daten erzeugt und das Modelltraining bis zu hunderte Petabytes an Daten erfordert.
- Successful completion of multiple PoCs with autonomous vehicle system developers
- Technology achieves 20%-50% video data savings in ML model training
- Solution maintains both visual quality and machine learning model stability
- Expansion into the fast-growing autonomous vehicles market
- None.
Insights
Beamr's video compression tech shows 20-50% data savings in autonomous vehicle applications, validating expansion beyond entertainment markets.
Beamr's announcement represents a strategic expansion of their video optimization technology into the autonomous vehicle (AV) sector, addressing a critical pain point in the industry. The company has completed multiple Proof of Concepts with AV system developers, with several successful validations demonstrating their technology's effectiveness in this new application.
The technical achievement is significant: Beamr's Content-Adaptive Bitrate technology can reduce video data used in AV machine learning training by
- Video is the primary data source for AV systems
- Individual vehicles generate terabytes of video data daily
- Training a single autonomous model requires tens to hundreds of petabytes of data
- Data management, storage, and infrastructure costs represent major expenses
The data compression benefits could potentially reduce infrastructure costs and processing time for AV developers. While the press release doesn't specify which or how many AV system developers participated in these PoCs, the validation in real-world testing environments represents a critical milestone in Beamr's market expansion strategy.
This development demonstrates Beamr's ability to adapt its core technology to emerging high-data applications beyond traditional entertainment markets. The autonomous vehicle sector represents a rapidly growing market with intense data management challenges—precisely the type of problem Beamr's technology is designed to solve. Their approach is particularly valuable because it maintains both human-perceptible quality and machine learning performance while significantly reducing data volume requirements.
Herzliya, Israel, July 18, 2025 (GLOBE NEWSWIRE) -- Beamr Imaging Ltd. (NASDAQ: BMR), a leader in video optimization technology and solutions, today announced a further update on its progress of validating Beamr content-adaptive, GPU-accelerated technology to the autonomous vehicles market following the initial successful launch of the Beamr solution for autonomous vehicles.
Over the past few months, Beamr engaged in multiple Proof of Concepts (PoCs) with autonomous vehicles system developers. Some of these PoCs were successful in further validating Beamr’s contribution to the autonomous vehicles (AV) industry.
The Beamr solution for autonomous vehicles demonstrates that it is not just keeping the visual quality of the video being perceptually identical to a human viewer, but also keeps the Machine Learning (ML) results stable to the extent that using video compression with Beamr Content-Adaptive Bitrate technology (CABR) yields
"We are encouraged by the progress that we have made so far with our AV offering, which has already been proven with successful PoCs with AV systems developers. We believe that this indicates the use of Beamr technology is indeed applicable to such fast growing markets, like the AV market." said Sharon Carmel, founder and CEO of Beamr
In the development of autonomous driving, video is the dominant data type. A single vehicle produces terabytes of video data daily. Training a single autonomous model may require tens to hundreds of petabytes, which is a costly challenge for autonomous vehicles and machine learning teams and which requires managing video data at scale, long-term storage and significant infrastructure investment.
For more details visit: beamr.com/autonomous
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
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.
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