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Beamr Releases White Paper on using its CABR Solution to Boost Vision AI

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Beamr (BEAMR) announced the release of a White Paper showcasing its Content Adaptive Bitrate (CABR) optimized encoding solution, which reduces video size without compromising perceptual quality, making video used for vision AI easier to handle. The solution aims to address the challenge of managing large video files and libraries for Machine Learning (ML) players, potentially cutting processing costs. Beamr's technology was tested on NVIDIA DeepStream SDK, showing a 40% reduction in video size without affecting ML results. The company plans to further explore the benefits of CABR in ML tasks.
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Herzliya Israel, Dec. 11, 2023 (GLOBE NEWSWIRE) -- Beamr, a leader in video optimization technology and solutions, today announced that it released a White Paper detailing how its Content Adaptive Bitrate (CABR) optimized encoding solution, which reduces video size but not perceptual quality, can make video used for vision AI easier to handle, thus reducing workflow complexity. Beamr is advancing on a new front - and reveals its capability to support Machine Learning for video.

Machine Learning (ML) for video processing is a field expanding at a fast pace, with a market already estimated at billions of dollars. One of the biggest pain points for ML players is managing extremely large video files and libraries. As the cluster of files grows bigger, they are faced with the challenging task of storing and transferring them at an increasing cost.

Results of a new case study show it may be possible to address the challenge and cut processing costs using Beamr’s technology.


Screen shot form Beamr Machine Learning experiment showing that true detection results are unaffected by replacing the source file (left) with the smaller, easier-to-transfer, optimized file (right)

In the new White Paper, Beamr shows that video files that were slimmed down by 40% on average - without losing their perceptual quality due to Beamr’s CABR technology - keep ML results unaffected.

The tests were conducted on NVIDIA DeepStream SDK - a tool for AI-based multi-sensor processing, video, audio and image understanding, which was a natural choice for Beamr as an NVIDIA Metropolis partner.

Tamar Shoham, Beamr CTO, said: "We decided to try out the Nvidia DeepStream SDK, which enables vision AI applications and services, combined with the recent Nvidia encoder enhanced with Beamr CABR. In the experiment, we demonstrated that the results of the DeepStream were unaffected by the video optimization process”.

“We are thankful to the Nvidia DeepStream team for supporting our research”, Shoham Added.

The results presented in the White Paper show that Beamr’s patent-proven and award winning technology - Content Adaptive Bitrate - can be applied to videos that undergo ML tasks such as object detection. In future work, Beamr plans to investigate the further potential benefits obtained when CABR is incorporated at the training stage and expand the experiments to include more model types and ML tasks.

Read the full White Paper: Beamr CABR Poised to Boost Vision AI

About Beamr

Beamr (Nasdaq: BMR) is a world leader in content adaptive video solutions. Backed by 53 granted patents, and winner of the 2021 Technology and Engineering Emmy® award and the 2021 Seagate Lyve Innovator of the Year award, Beamr's perceptual optimization technology enables up to a 50% reduction in bitrate with guaranteed quality. www.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 April 24, 2023 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:

investorrelations@beamr.com


Beamr announced its Content Adaptive Bitrate (CABR) optimized encoding solution in the White Paper, showcasing its ability to reduce video size without compromising perceptual quality for vision AI applications.

Beamr's technology aims to address the challenge of managing large video files and libraries for ML players, potentially cutting processing costs.

The tests showed a 40% reduction in video size without affecting ML results, demonstrating the potential benefits of Beamr's Content Adaptive Bitrate (CABR) technology.

Beamr plans to further investigate the benefits of CABR when incorporated at the training stage and expand the experiments to include more model types and ML tasks.
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