AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
VMAFX — perceptual video quality assessment. Modernized fork-evolved with SYCL/CUDA/HIP/Metal backends, tiny-AI models, MCP server, cloud-native (k8s/Helm) deployment. BSD-2-Clause-Patent.
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Developer toolsPython · CGitHub @VMAFxOnlineOwner unconfirmed
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Loading the full README. The saved excerpt is shown below.
VMAFx is a fork of Netflix/vmaf for
perceptual video quality assessment. It extends libvmaf with GPU and SIMD
backends, additional metrics, and tools for scoring and model workflows.
Full documentation ·
Documentation source
Follow the installation and source-build guide
for your platform. For a container workflow, see Docker.
After installation, compare a matching reference and distorted Y4M pair:
See the CLI reference for raw YUV geometry, model selection,
backend selection and output options. For compressed inputs such as MP4, use
FFmpeg integration.Read the full README ↗The full README is unavailable right now. Read it at the original link. The saved excerpt is shown below.
VMAFx is a fork of Netflix/vmaf for
perceptual video quality assessment. It extends libvmaf with GPU and SIMD
backends, additional metrics, and tools for scoring and model workflows.
Full documentation ·
Documentation source
Follow the installation and source-build guide
for your platform. For a container workflow, see Docker.
After installation, compare a matching reference and distorted Y4M pair:
See the CLI reference for raw YUV geometry, model selection,
backend selection and output options. For compressed inputs such as MP4, use
FFmpeg integration.Read the full README ↗