Perceptual video quality assessment based on multi-method fusion.
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README.md

VMAF - Video Multi-Method Assessment Fusion

Build Status

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.

News

  • (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.
  • (6/19/18) Added a 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.
  • (6/5/18) Speed optimization to vmafossexec: 1) support multi-threading (e.g. use --thread 0 to use all cores), 2) support frame sampling (e.g. use --subsample 5 to calculate VMAF on one of every 5 frames).
  • (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 --recursive and add sureal/python/src to PYTHONPATH.

Frequently Asked Questions

Refer to the FAQ page.

Usages

The VDK package offers a number of ways for a user to interact with the 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 ffmpeg2vmaf that can pipe FFmpeg-decoded raw videos to VMAF. Unlike other command lines, ffmpeg2vmaf can 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, vmafossexec offers 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 - 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.

Datasets

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.

Models

Besides the default VMAF model which predicts the quality of videos displayed on a 1080p HDTV in a living-room-like environment, VDK also includes a number of additional models, covering phone and 4KTV viewing conditions. Refer to the models page for more details.

Confidence Interval

Since VDK v1.3.7 (June 2018), we have introduced a way to quantify the level of confidence that a VMAF prediction entails. Each VMAF prediction score now can come with a 95% confidence interval (CI), which quantifies the level of confidence that the prediction lies within the interval. Refer to the VMAF confidence interval page for more details.

References

Refer to the references page.