Helter Skelter
2 weeks ago
Beamr to enable real-time video streaming over 5G with NVIDIA Holoscan for Media
April 09 2024 - 12:12PM
Beamr Imaging Ltd. (NASDAQ: BMR), a leader in video optimization technology and solutions, today announced that it plans to demonstrate a real-time video optimization application with the NVIDIA Holoscan for Media, a software-defined platform that enables developers to easily build live media applications, at NAB Show 2024, a premier event for the broadcasting, streaming and media industry in Las Vegas, Nevada, on April 13-17, 2024.
5G increased bandwidth is not sufficient to support the always-growing demand for real-time video streaming required for live event transmission and other use cases across a wide set of industries, including edge computing, IoT, and building robots, drones, autonomous vehicles and automated cameras. With these industries’ increasing use of ultra-high-definition video (UHD or 4K), Internet suppliers are struggling to support the upload speed required to stream at the right video quality.
Beamr’s proven ability to significantly reduce video file size without compromising quality, now demonstrated in real-time video streaming, may provide a solution to this challenge. The real-time video optimization is based on Beamr’s perceptual optimization technology (CABR), backed by 53 patents, already operational for various use cases.
The optimized real-time streaming, using Beamr’s optimization technology, is planned to be demonstrated using NVIDIA Holoscan for Media - a software-defined platform for building and deploying applications for live media. Holoscan for Media revolutionizes application development for this rapidly evolving market by providing an IP-based, cloud-native architecture that is not constrained by dedicated hardware, environments, or locations. Beamr CABR applied in Holoscan for Media fully supports AV1, combining latest codec efficiency from AOM and Beamr quality optimization for minimal bandwidth with optimal streaming quality.
Beamr CEO Sharon Carmel said: “There is an urgent need for solutions for live-streaming video, edge products, or the Internet of Things in the rapidly growing video world. Beamr’s ability to lower bitrate and enable 4K video streaming at scale and over 5G has the potential to grow usage, improve user experience, and lower costs for such enterprises.”
“Livestreams are being revolutionized by emerging workflows that demand greater efficiency and flexibility,” said Sepi Motamedi, Senior Product Marketing Manager for Live Media Solutions at NVIDIA. “By combining Beamr’s technologies with NVIDIA Holoscan for Media, broadcasters can harness real-time video optimizations to deliver the next generation of viewing experiences.”
NAB Show is produced annually by the National Association of Broadcasters and presents the forefront products and technology for the broadcasting industry. Beamr is inviting all NAB Show attendees interested in Beamr tech’s real-time video streaming capabilities to schedule a meeting. For registration, please click the link.
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
Helter Skelter
1 month ago
👉️ Beamr Boost for Machine Learning: Accelerating Neural Networks Training
March 20 2024 - 07:00AM
Beamr Imaging Ltd. (NASDAQ: BMR), a leader in video optimization technology and solutions, today announced that it has released the results of a case study which highlights how Beamr tech enables accelerated machine learning training by using significantly smaller video files and without any negative impact on the video artificial intelligence (AI) process.
Machine learning for video is becoming an increasingly significant technology for businesses. But the players in this expanding arena face critical pain points, like storage and bandwidth bottlenecks or the difficulty to reach acceptable training and inferencing speeds.
In this case study, Beamr’s R&D team showed that training an AI network using video files compressed and optimized through Beamr’s Content-Adaptive Bitrate technology produced results that are as good as training the network with the original, larger files. The AI network was trained to fulfill the task of action recognition, such as distinguishing between people who are walking, running, dancing or doing many other day-to-day actions.
Beamr CTO, Tamar Shoham, explained: “It was important to us to define a test case that really uses the fact that the content is video, instead of an image. When viewing individual frames, it is not possible to differentiate between frames captured during walking and running, or between someone jumping or dancing. Therefore, in order to classify videos according to the action they show, the temporal component is needed, which is why 'action recognition' was our task of choice”.
The video files used for machine learning training were optimized by Beamr Cloud, reducing file sizes by 24%-67%. Such a reduction is beneficial when storing video files for future use and possibly performing other manipulations on them. Recently-launched Beamr cloud is an optimization and modernization software-as-a-service (SaaS) that enables automated, efficient and fast video processing, through no-code processes or customized pipelines to meet specific user needs.
Training performed with the smaller video files optimized by Beamr tech, provided results which were equivalent to those obtained with the larger and non-optimized files (for more details about the experiment, see the full case study).
The case study is part of Beamr’s ongoing commitment to accelerate adoption and increase accessibility of machine learning for video and video analysis solutions. A previous case study focused on the AI network inference stage, which is the phase of drawing conclusions from an AI network that has already been trained. The previous experiment found that video files that were downsized by 40% on average streamlined machine learning processes. This allowed significant savings in storage and costs.
The current case study covers the more challenging task of training a neural network for action recognition in video. In coming months, the Beamr R&D team plans to expand the initial experiment described above to large scale testing, including neural networks that operate in the cloud using GPUs.
Accelerate the Adoption of Machine Learning for Video
The AI Network was Trained to Distinguish Between People who are Doing Many Day-to-Day Actions
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. http://www.beamr.com
handsdo
1 month ago
Beamr presents accelerated video Al workflows at NVIDIA
GTC
3/19/2024 6:00am
Herzliya Israel, March 19, 2024 (GLOBE NEWSWIRE) -- Beamr Imaging Ltd. (NASDAQ: BMR), a leader in video optimization technology and solutions, today announced it will present accelerated video AI workflows at NVIDIA GTC. GTC is a global AI conference for developers and business minds shaping the future of artificial intelligence (AI) and accelerated computing. At GTC, Beamr will highlight its plans for integrating AI workflows into Beamr Cloud - Beamr’s recently launched video software-as-a-service (SaaS) solution powered by NVIDIA technology - allowing easy and wide access to advanced video processing.
AI startups or even seasoned AI firms face challenges training massive video AI models. This is because of the complexity of the size and resolution of large video files. Beamr’s vision is to lower the barrier of entry to the highest level of video AI capabilities. Beamr plans to offer AI and machine learning players a variety of solutions that may enable them to overcome critical pain points, such as slow training and inferencing speed, large numbers of GPUs required to complete a task, data storage, networking bottlenecks and high associated costs.
Beamr plans to focus its research and development efforts on providing AI and generative AI players access to video AI workflows, tailored to their needs, through Beamr Cloud. Recently, Beamr released a technical paper showing that machine learning workflows benefit from the ability to create a compressed file that looks exactly the same as the original one. Video files that were downsized by 40% on average had streamlined machine learning processes and allowed significant savings in storage and overall costs without any negative effect on training and inference results.
Beamr’s video optimization tech - integrated with NVIDIA’s 8th-generation GPU encoder (NVENC), available on NVIDIA T4 Tensor Core and NVIDIA RTX GPUs, and NVIDIA L4 Tensor Core, L40 and L40S GPUs - aims to accelerate video AI workflows and enhance video pre-training, training and inference capabilities in AI pipelines. NVENC SDK 12.1 added an API that allows external control and enables users to tightly integrate hardware encoders for AVC and HEVC video formats. In addition, it supports AOMedia Video 1 (AV1), an efficient emerging video format.
Last month, Beamr launched Beamr Cloud, enabling high-quality, high-scale video processing at attractive pricing. Beamr Cloud, operating on Amazon’s AWS and powered by NVIDIA technology, targets the expanding markets of AI, user-generated content, autonomous cars, online video editing, podcast platforms and more. Beamr Cloud is powered by NVIDIA technologies like NVIDIA CUDA, NVIDIA DeepStream SDK, and NVENC, and supports reductions in video training, inferencing, and extract, transform and load (ETL) times, while delivering cost savings in bandwidth and storage.
Image: Beamr Cloud - High-Quality, High-Scale Video Processing at Attractive Pricing
Faster Extract, Transform and Load (ETL) with Beamr Optimized Video
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 U.S. Securities and Exchange Commission (the “SEC”) on March 4, 2024 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
tedpeele
1 month ago
In February 2024, we launched our Beamr Cloud Video, or the Beamr Cloud, Software-as-a-Service, or SaaS, solution, a cloud based HW-Accelerated CABR solution, which we expect will allow end-users to enjoy significant end-user storage and networking cost savings. Our Cloud Video SaaS is currently operating over and integrated with Amazon Web Services, or AWS, with plans to extend our services to other cloud platforms, and is powered by NVIDIA graphics processing unit, or GPUs. We have managed to complete certain features, such as codec modernization and resize transformations.
Our current product line is mainly geared to the high end, high quality media customers and we count among our enterprise customers Netflix, Snapfish, ViacomCBS, TAG, VMware, Genesys, Deluxe, Vimeo, Encoding.com, Citrix, Walmart, Photobox, Antix, Dalet, and other leading media companies using video and photo solutions. Due to the high cost and complexity of deploying our existing software solutions and the long sales lead times, we have a made a strategic decision to focus our resources on the development and commercialization of our next-generation product, the Beamr Cloud, a SaaS solution that is designed, based on our own internal testing, to be up to 10x more cost efficient than our existing software-based solutions, resulting in reduced media storage, processing and delivery costs.
We collaborated with NVIDIA, a multinational technology company and a leading developer of GPUs, with an annual revenue of $60.9 billion for the fiscal year 2024, to develop the Beamr Cloud SaaS solution, the world’s first GPU accelerated encoding solution powered with our CABR, which will allow fast and easy end-user deployment combined with superior video compression rates. Our CABR software executes directly on NVIDIA GPU cores and interacts with the NVIDIA video accelerator encoder known as NVENC. NVIDIA NVENC is a high-quality, high-performance hardware video encoder that is built into most NVIDIA GPUs. NVENC offloads video encoding to hardware, and provides extreme performance for applications such as live video encoding, cloud gaming and cloud storage. NVIDIA GPUs with NVENC are available on all major cloud platforms. We plan to further collaborate with NVIDIA on further development of our the Beamr Cloud SaaS solution.
The first version of the integrated video optimization engine was ready at the end of the first quarter of 2023. Following this, we launched the first beta version of the cloud based SaaS platform and began testing it with beta customers in June 2023. After the initial release, we launched the second and third beta versions of the cloud based SaaS platform in September 2023 and October 2023, respectively, as we build up to the commercial launch of the platform. Following that, we commercially launched the Beamr Cloud SaaS solution in February 2024 and expect that following release, end-users of the solution will enjoy significant end-user storage and networking cost savings. Using the Beamr Cloud SaaS solution will potentially reduce their return on investment for storage optimization to approximately four months, compared to approximately two years with our existing software encoder solutions. Our Cloud Video SaaS is currently operating over and integrated with AWS with plans to extend our services to other cloud platforms, and is powered by NVIDIA GPUs. We have managed to complete certain features, such as codec modernization and resize transformations, and we plan to offer additional capabilities, such as Artificial Intelligence, or AI, specific workflows that are optimized for Machine Learning, or ML, and AI in an effort to position ourselves to be at the forefront of innovation in the video processing landscape for different AI purposes.
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