Tamar Shoham
Introduction Machine learning for Video is an expanding field, garnering vast interest, with generative AI for video picking up speed. However there are significant pain points for these technologies such as storage and bandwidth bottlenecks when dealing with video content, as well as training and inferencing speeds. In the following case study, we show that […]
Tamar Shoham
By reducing video size but not perceptual quality, Beamr’s Content Adaptive Bit Rate optimized encoding can make video used for vision AI easier to handle thus reducing workflow complexity
Tamar Shoham
Easy & Safe Codec Modernization with Beamr using Nvidia GPUs Following a decade where AVC/H.264 was the clear ruler of the video encoding world, the last years have seen many video coding options battling to conquer the video arena. For some insights on the race between modern coding standards you can check out our corresponding […]
Tamar Shoham
The proliferation of AI-generated visual content is creating a new market for media optimization services, with companies like Beamr well positioned to help businesses optimize their video content for reduced storage, faster delivery, and better user experiences. We are living in a brave new world, where any image and video content we can imagine is […]
Tamar Shoham
2023 is a very exciting year for Beamr. In February Beamr became a public company on NASDAQ:BMR on the premise of making our video optimization technology globally available as a SaaS. This month we are already announcing a second milestone for 2023: Release of the Nvidia driver that enables running our technology on the Nvidia […]
Tamar Shoham
Introduction There are several different video codecs available today for video streaming applications, and more will be released this year. This creates some confusion for video services who need to select their codec of choice for delivering content to their users at the best quality and lowest bitrate, also taking into account the encode compute […]
Tamar Shoham
At the heart of Beamr’s closed-loop content-adaptive encoding solution (CABR) is a patented quality measure. This measure compares the perceptual quality of each candidate encoded frame to the initial encoded frame. The quality measure guarantees that when the bitrate is reduced the perceptual quality of the target encode is preserved. In contrast to general video […]
Tamar Shoham
Going Inside Beamr’s Frame-Level Content-Adaptive Rate Control for Video Coding When it comes to video, the tradeoff between quality and bitrate is an ongoing dance. Content producers want to maximize quality for viewers, while storage and delivery costs drive the need to reduce bitrate as much as possible. Content-adaptive encoding addresses this challenge, by striving […]
