Beamr, a leading provider of content-adaptive video solutions, has announced a groundbreaking compression technology that promises to boost machine learning for video. This development is expected to revolutionize the field of machine learning and artificial intelligence for video, which has already achieved remarkable advancements and has significant potential for future growth.
One of the major challenges in this field is managing large video files and libraries. Video files are inherently large, and training computer networks to recognize moving objects requires a vast amount of data. With each movement, objects in a video change in appearance, including their shape, size, and angle. As a result, computer networks need to analyze countless videos to learn how to accurately identify different objects. This poses significant difficulties in terms of managing, storing, and transferring large clusters of video files.
The high costs associated with managing these large files and libraries have been a major impediment to the growth of companies and start-ups involved in machine learning for video. However, Beamr’s compression technology offers a solution to this problem. By scanning each and every frame of a video file, Beamr’s technology can determine how much the file can be compressed without compromising its quality.
An experiment conducted by Beamr’s Chief Technology Officer, Tamar Shoham, demonstrated the effectiveness of the technology in reducing the size of video files. On average, the files were downsized by 40%, resulting in significant savings in storage and costs. Importantly, the experiment also showed that the compressed files yielded similar results when performing people detection tasks, indicating that the quality of the videos was maintained after compression.
The experiments were conducted using NVIDIA DeepStream SDK, a tool for AI-based multi-sensor processing, video, audio, and image understanding. This collaboration with NVIDIA allows Beamr to leverage the capabilities of DeepStream SDK in optimizing machine learning workflows.
Beamr’s technology, which has won numerous awards and is backed by 53 patents, aims to achieve the best trade-off between video quality and compression. Whether used for streaming films on platforms like Netflix or for professional applications that require detailed analysis of every pixel, Beamr’s technology allows for significant reductions in bitrate while maintaining high-quality video.
Machine learning for video is a rapidly expanding field within the larger computer vision market, which is already estimated to be worth over $20 billion and is expected to experience exponential growth in the coming years. With Beamr’s groundbreaking compression technology, the industry can overcome one of its major challenges and unlock its full potential. Further research and evaluation of the technology’s contribution to machine learning workflows are already underway by Beamr’s research and development teams.
In conclusion, Beamr’s compression technology is set to revolutionize machine learning for video by addressing the challenges associated with managing large video files and libraries. By significantly reducing the size of video files without compromising quality, Beamr’s technology offers cost savings and streamlines machine learning processes. With its proven effectiveness and ongoing research, Beamr is well-positioned to advance the field of machine learning for video and contribute to its continued growth and success.
