VMAF is a perceptual video quality assessment algorithm developed by Netflix. VMAF Development Kit (VDK) is a software package that contains the VMAF algorithm implementation, as well as a set of tools that allows a user to train and test a custom VMAF model. For an overview, read this tech blog post, or this slide deck.
- (6/19/18) Each VMAF prediction score now comes with a 95% confidence interval (CI), which quantifies the level of confidence that the prediction lies within the interval. Refer to this section for details.
- (6/19/18) Added 4K VMAF model under
model/vmaf_4k_v0.6.1.pkl, which predicts the subjective quality of video displayed on a 4KTV and viewed from the distance of 1.5X the display height. Read this section for details. - (6/5/18) Speed optimization to
vmafossexec: 1) support multi-threading (e.g. use--thread 0to use all cores), 2) support frame sampling (e.g. use--subsample 5to calculate VMAF on one of every 5 frames). See this section for details. - (1/20/18) Moved custom subjective models into a submodule named sureal. If you pull the latest changes, you will have to pull the submoddule by
git submodule update --init --recursiveand addsureal/python/srctoPYTHONPATH. - (8/12/17) VMAF is now packaged into a library call
libvmafand can be called from a C/C++ program directly. See this document for details. Using this library, VMAF is now included as a filter in FFmpeg main branch, and can be configured using:./configure --enable-libvmaf.
Refer to the FAQ page.
THe VDK package offers multiple ways for a user to interact with VMAF algorithm implementations. The core feature extraction library is written in C. The rest scripting code including the classes for machine learning regression, training and testing VMAF models and etc., is written in Python. Besides, there is C++ "wrapper" code partially replicating the logic in the regression classes, such that the VMAF prediction (excluding training) is fully implemented in C/C++.
There are a number of ways one can use the pakcage:
- VMAF Python library offers full functionalities including running basic VMAF command line, running VMAF on a batch of video files, training and testing a VMAF model on video datasets, and visualization tools, etc. It also provides a command line tool
ffmpeg2vmafthat can pipe FFmpeg-decoded raw videos to VMAF. Unlike other command lines,ffmpeg2vmafcan take compressed video bitstreams as input. vmafossexec- a C++ "wrapper" executable offers running the prediction part of the algorithm in full, such that one can easily deploy VMAF in a production environment without needing to configure the Python dependancies. Additionally,vmafossexecoffers a number of exclusive features, such as 1) speed optimization using multi-threading and skipping frames, 2) optionally computing PSNR, SSIM and MS-SSIM metrics in the output.libvmaf- a static library offers an interface to incorporate VMAF into your C/C++ code. Using this library, VMAF is now included as a filter in FFmpeg main branch, and can be configured using:./configure --enable-libvmaf.- VMAF Dockerfile generates a VMAF docker image from the VMAF Python library. Refer to this document for detailed usages.
We also provide two sample datasets including the video files and the properly formatted dataset files in Python. They can be used as sample datasets to train and test custom VMAF models.
Besides the default VMAF model model/vmaf_v0.6.1.pkl, VDK also includes a number of additional models, covering phone and 4KTV viewing conditions, as well as models that provide additional prediction confidence intervals. Refer to the models page for more details.
