Standard encoders target a bitrate budget regardless of what each frame contains. CABR works differently - it analyzes every frame, and runs a closed-loop evaluation to find the minimum bitrate that is perceptually identical to the reference frame. Backed by 53 patents, quality measure validated against human perception through large-scale subjective testing.

CABR uses a patented perceptual quality measure, to guarantee the output is perceptually identical to the source at the lowest possible bitrate. The result: up to 50% file size reduction without perceptual loss for human viewers, and without measurable impact on machine vision model accuracy.

CABR is codec-agnostic. It currently supports AVC, HEVC, and AV1, and is compatible with existing encoding infrastructure without re-architecture. GPU-accelerated deployment is available on NVIDIA hardware across all three codecs.

For human viewers, CABR's quality measure was developed and verified through large-scale subjective testing - crowdsourced viewers, forced-choice methodology, statistically significant results (learn more in VISTA blog). For machine vision, Beamr ran a series of ML-safe benchmarks across the AV pipeline - object detection with less than 2% difference from uncompressed baseline object detection at mAP 0.96, depth estimation error reduction, and foundation model pipeline validation - demonstrating that model accuracy is preserved and can even be improved when models are trained on CABR-compressed data.

No. CABR integrates with existing workflows. You can ingest any input - outputs are standard-compliant (AVC, HEVC, AV1). There is no requirement to change upstream or downstream components. Beamr's VidOps team, our video operations experts, supports integration to ensure easy deployment.

Yes, both. CABR is available for on-premises, private cloud, and public cloud deployment, including AWS and GPU-accelerated infrastructure. Deployment options are aligned to customer requirements - from single-pipeline integration to petabyte-scale production deployments.