Perceptual video quality assessment based on multi-method fusion.
Clone or download
Latest commit a654f6f Sep 14, 2018
Permalink
Failed to load latest commit information.
feature Fix implicit declaration of functions (#225) Sep 12, 2018
libsvm Feature/revamp2 (#23) Sep 28, 2016
matlab Add ST-RREDOpt (time optimized), ST-MAD feature extractors, quality r… Sep 5, 2018
model Feature/add 4k model and ci (#175) Jun 20, 2018
pthreads Feature/VisualStudio2015 support (#92) Jul 13, 2017
ptools Replace with regular Makefile that includes active Makefile (#134) Dec 22, 2017
python Fix extratest. Sep 14, 2018
resource Feature/remove sureal submodule (#226) Sep 14, 2018
workspace Misc. Apr 30, 2017
wrapper Fix implicit declaration of functions (#225) Sep 12, 2018
.gitattributes Add .gitattributes (#127) Dec 4, 2017
.gitignore Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
.travis.yml Feature/remove sureal submodule (#226) Sep 14, 2018
CHANGELOG.md Feature/remove sureal submodule (#226) Sep 14, 2018
CONTRIBUTING.md Update documentation clean up repo (#182) Jun 25, 2018
Dockerfile Feature/remove sureal submodule (#226) Sep 14, 2018
FAQ.md Update doc (#195) Jul 16, 2018
LICENSE Update documentation clean up repo (#182) Jun 25, 2018
Makefile Feature/vmaf packaging (#108) Aug 17, 2017
NOTICE.md Update VERSION; add CHANGELOG. May 24, 2018
OSSMETADATA Update versions etc. Jun 2, 2016
README.md Feature/remove sureal submodule (#226) Sep 14, 2018
VERSION Feature/remove sureal submodule (#226) Sep 14, 2018
extratest Feature/remove sureal submodule (#226) Sep 14, 2018
ffmpeg2vmaf Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
libtest Feature/remove sureal submodule (#226) Sep 14, 2018
run_psnr Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
run_testing Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
run_vmaf Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
run_vmaf_in_batch Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
run_vmaf_training Refactored code in preparation for publishing to pypi (#66) Feb 27, 2017
unittest Feature/remove sureal submodule (#226) Sep 14, 2018
vmaf.sln Feature/VisualStudio2015 support (#92) Jul 13, 2017

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

  • (9/13/18) SUREAL is no longer a submodule to VMAF.
  • (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).

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.
  • 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 --enable-version3. See this section for details. Using FFmpeg with libvmaf allows passing in compressed video bitstreams directly to VMAF.
  • 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.