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VMAF requires frame alignment between the reference and the distorted videos, and so you have to guarantee that the frame rates, scan mode, and video durations match. If it is not the case, you can apply the same scalefilter to the reference also: ffmpeg -i -i -lavfi 'scale=3840:2160:flags=bicubic scale=3840:2160:flags=bicubic libvmaf=model_path=/usr/local/share/model/VMAF_4k_v0.6.1.pkl' -f null. The above examples consider that the reference video already matches with the resolution expected by the VMAF model. ffmpeg -i -i -lavfi 'scale=3840:2160:flags=bicubic libvmaf=model_path=/usr/local/share/model/VMAF_4k_v0.6.1.pkl' -f null. Similarly, to use the 4K model we need to scale the distorted video to 3840x2160. The mismatch between the source and destination videos’ resolution can be solved using bicubic interpolation in FFmpeg’s scale filter.įor example, to use the HD model ( VMAF_v0.6.1.pkl) we need to scale the distorted video (if it isn’t 1920x1080) by using the following FFmpeg command – ffmpeg -i -i -lavfi 'scale=1920:1080:flags=bicubic libvmaf=model_path=/usr/local/share/model/VMAF_v0.6.1.pkl' -f null.

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In this tutorial, we will go through a series of recommendations using FFmpeg-based examples to show you how to normalize your streams for using VMAF.įinally, we introduce easyVMAF, an open-source tool to automate your VMAF computations. Hence in practice, the videos have to be normalized/equalized if the reference and distorted videos do not meet the above-mentioned requirements. The reference and distorted videos’ resolution has to match, which often requires a robust up/down-scaling procedure.

The scan mode (interlaced/progressive) has to match they have to have the same duration they have to have the same frame-rate. The reference and distorted videos need to be frame-synchronized.Inspite of several tools being available to compute VMAF, it is often challenging to adhere to the strict set of requirements that the VMAF tool imposes. Some of these requirements are – Note: You can find Installation procedures for FFmpeg, VMAF, and usage here on As a result, VMAF is available via several open-source packages such as the VMAF python library, VMAFossexec (C executable), a VMAF docker image, libvmaf (C library), and via FFmpeg compiled with libvmaf. Several 3rd-party tools have been built on top of VMAF with the participation of the open-source community. For an in-depth explanation of VMAF, please read the official Netflix blog. The advantage of VMAF lies in the fact that it seeks to mimic the viewer’s perception (or human visual system) instead of purely objective metrics such as PSNR or SSIM.

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VMAF is a full reference metric that compares the reference (or source) and distorted video sequences to predict the subjective video quality. In this article, guest author Gabriel Dávila Revelo introduces us to a tool ( easyVMAF) that he’s developed to make VMAF computations easier and straightforward. However, running VMAF can get tricky in some situations and lead to wrong results. VMAF is one of the most popular tools for video quality assessment, and it is well on its way to becoming a standard reference metric for the video industry.