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
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.
“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
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
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