Netflix has open-sourced a new version of its video quality metric, VMAF v1. VMAF is crucial for optimizing video encoding and ensuring a good viewing experience for members. VMAF v1 addresses limitations of the previous version, VMAF v0, to more accurately assess visual quality. Key improvements include enhanced sensitivity to compression artifacts by adding the AIM component. VMAF v1 also allows for a single model to be used across various viewing conditions by adjusting feature values based on viewing distance. This approach improves accuracy and generalizability over older mapping functions. Banding artifacts are now addressed by integrating the CAMBI feature, and chroma artifacts are handled by modifying SpEED-QA for chroma channels. The no-enhancement gain (NEG) mode is now enabled by default to preserve creative intent and is used in codec development. VMAF v1 features an improved motion feature with a hard threshold and an option for a larger temporal window to better handle high frame rates. The new version includes multiple models for different resolutions and viewing distances, such as standard 1080p, phone, and 4K. VMAF v1 has been calibrated to align with v0 scores, preserving interpretability while offering enhanced accuracy. The new version also boasts reduced computational complexity, making it faster than VMAF v0. Netflix encourages the community to test VMAF v1, report issues, and contribute to its ongoing development.