Visual Information Fidelity (VIF) is an information-theoretic full-reference quality metric that treats the original frame as a source of visual information and asks how much of that information survives compression to reach the viewer's eye, modelled on how the human visual system takes in a scene. It is computed at several spatial scales — four, coarse to fine in the VMAF implementation — so it notices both broad and fine losses, and it is the judge that reacts when compression strips away the subtle texture of skin or foliage. VIF earned its place as one of the elementary features fused inside the classic VMAF (v0) design, alongside the detail-loss metric and a motion feature, where a trained model learned how to weigh it. Its notable twist is that the 2026 VMAF v1 generation drops VIF: it was costly to compute and, once other features improved, no longer added meaningful accuracy, so removing it made VMAF both more accurate and faster — proof that even a strong feature can become redundant.
The handbook · 7 volumes
Fora Soft Video Engineering Handbook
Visual Information Fidelity (VIF) still matters because it is one of the features inside VMAF, so it shapes many quality decisions indirectly. In our seven-volume handbook on the video stack, Volume 1 is where to start for VIF, explaining full-reference metrics, VMAF's fused features, SSIM and PSNR. Written by Nikolay Sapunov, CEO at Fora Soft.
Need to understand what VIF contributes to your VMAF scores?
We will break down your VMAF results by feature, including VIF, and explain which encoder changes moved them. 250+ video products shipped since 2005.
