Merge pull request #1 from Netflix/master

Updating to the latest master branch
Netflix · Feb 14, 2019 · 321fb7a · 321fb7a
2 parents 0c2696d + c3409bd
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,5 +1,12 @@
 # Change Log
 
+## (1/31/2019) [1.3.13]
+
+**New features:**
+- Optimized C code for speed. Running in multithreading mode, `vmafossexec` achieves ~40% run time reduction compared to the previous version.
+- Printed out individual vmaf bootstrap scores in text file from `vmafossexec`.
+- refactored windows solution (#283) (#284) (#285) (#291) (#298).
+
 ## (12/17/2018) [1.3.11]
 
 **New features:**

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -8,7 +8,7 @@ By contributing your code, you agree to license your contribution under the term
 
 ```
 /**
- * Copyright 2016-2018 the original author or authors.
+ * Copyright 2016-2019 the original author or authors.
  * 
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.

diff --git a/FAQ.md b/FAQ.md
@@ -56,12 +56,12 @@ A: This is due to the slightly different workflows used by `run_vmaf_training` a
 
 ### Q: How do I use VMAF with downscaled videos?
 
-If you have a distorted video that was scaled down (e.g. for adaptive streaming) and want to calculate VMAF, you can use ffmpeg with `libvmaf` to perform the re-scaling for you.
+If you have a distorted video that was scaled down (e.g. for adaptive streaming) and want to calculate VMAF, you can use FFmpeg with `libvmaf` to perform the re-scaling for you.
 
 For example, to upscale the distorted video to 1080p:
 
 ```
-ffmpeg -i main.mpg -i ref.mpg -filter_complex "[0:v]scale=1920:1080[main];[main][1:v]libvmaf" -f null -
+ffmpeg -i main.mpg -i ref.mpg -filter_complex "[0:v]scale=1920x1080:flags=bicubic[main];[main][1:v]libvmaf" -f null -
 ```
 
 This scales the first input video (`0:v`) and forwards it to VMAF (`libvmaf`) with the label `main`, where it is compared against the second input video, `1:v`.

diff --git a/LICENSE b/LICENSE
@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright 2016-2018 Netflix, Inc.
+   Copyright 2016-2019 Netflix, Inc.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

diff --git a/README.md b/README.md
@@ -2,16 +2,15 @@ VMAF - Video Multi-Method Assessment Fusion
 ===================
 [![Build Status](https://travis-ci.org/Netflix/vmaf.svg?branch=master)](https://travis-ci.org/Netflix/vmaf)
 
-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](http://techblog.netflix.com/2016/06/toward-practical-perceptual-video.html) tech blog post, or [this](resource/doc/VMAF_ICIP17.pdf) slide deck.
+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. Read [this](https://medium.com/netflix-techblog/toward-a-practical-perceptual-video-quality-metric-653f208b9652) techblog post for an overview, or [this](https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51ee9ed12) post for the latest updates and tips for best practices.
 
 ## News
 
+- (1/31/19) Optimized C code for speed. Running in multithreading mode, `vmafossexec` achieves ~40% run time reduction compared to the previous version.
 - (11/19/18) Added a BD-rate calculator implementation. See more details [here](resource/doc/VMAF_Python_library.md#bd-rate-calculator).
 - (10/25/18) We have published our [second techblog on VMAF](https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51ee9ed12), with recommendations on best practices.
 - (9/13/18) [SUREAL](https://github.com/Netflix/sureal) is no longer a submodule to VMAF.
 - (6/19/18) Each VMAF prediction score now comes with a 95% [confidence interval (CI)](resource/doc/conf_interval.md), which quantifies the level of confidence that the prediction lies within the interval.
-- (6/19/18) Added a [4K VMAF model](resource/doc/models.md/#predict-quality-on-a-4ktv-screen-at-15h) 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`](resource/doc/vmafossexec.md): 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
 
@@ -25,8 +24,9 @@ There are a number of ways one can use the package:
 
   - [VMAF Python library](resource/doc/VMAF_Python_library.md) 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](resource/doc/vmafossexec.md) 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 dependencies. 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](resource/doc/libvmaf.md) offers an interface to incorporate VMAF into your C/C++ code. Using this library, VMAF is now included as a filter in [FFmpeg](http://ffmpeg.org/) main branch, and can be configured using: `./configure --enable-libvmaf --enable-version3`. See [this](https://ffmpeg.org/ffmpeg-filters.html#libvmaf) section for details. Using FFmpeg with `libvmaf` allows passing in compressed video bitstreams directly to VMAF.
+  - [`libvmaf.a` - a static library](resource/doc/libvmaf.md) offers an interface to incorporate VMAF into your C/C++ code. Using this library, VMAF is now included as a filter in [FFmpeg](http://ffmpeg.org/) main branch, and can be configured using: `./configure --enable-libvmaf --enable-version3`. See [this](resource/doc/libvmaf.md#use-libvmaf-with-ffmpeg) section for details. Using FFmpeg with `libvmaf` allows passing in compressed video bitstreams directly to VMAF.
   - [VMAF Dockerfile](Dockerfile) generates a VMAF docker image from the [VMAF Python library](resource/doc/VMAF_Python_library.md). Refer to [this](resource/doc/docker.md) document for detailed usages.
+  - Build VMAF on Windows: follow instructions on [this](resource/doc/BuildForWindows.md) page.
 
 ## Datasets
 
@@ -40,6 +40,10 @@ Besides the default VMAF model which predicts the quality of videos displayed on
 
 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](resource/doc/conf_interval.md) page for more details.
 
+## Matlab Functionality
+
+Besides the Python/C/C++ part of the repository, we also introduced a number of algorithms that are implemented in Matlab. For example, users can calculate ST-RRED, ST-MAD, SpEED-QA, and BRISQUE. For more details, see the [Matlab Usage](resource/doc/matlab_usage.md) page for more details.
+
 ## References
 
 Refer to the [references](resource/doc/references.md) page.
diff --git a/VERSION b/VERSION
@@ -1,2 +1,2 @@
-VMAF Development Kit (VDK) Version 1.3.11
+VMAF Development Kit (VDK) Version 1.3.13
 VMAF Version 0.6.1
diff --git a/feature/examples.bat b/feature/examples.bat
@@ -0,0 +1,33 @@
+rem refer feature/examples
+
+vmaf
+echo ""
+
+echo "run adm:"
+vmaf adm yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run ansnr:"
+vmaf ansnr yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run motion:"
+vmaf motion yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run vif:"
+vmaf vif yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run all:"
+vmaf all yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run psnr:"
+psnr yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run 2nd moment:"
+moment 2 yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv 576 324
+
+echo "run ssim:"
+ssim yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "run ms_ssim:"
+ms_ssim yuv420p ..\..\python\test\resource\yuv\src01_hrc00_576x324.yuv ..\..\python\test\resource\yuv\src01_hrc01_576x324.yuv 576 324
+
+echo "done."
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