@eyevinn/autovmaf

autovmaf - A toolkit to automatically encode multiple bitrates and perform automated VMAF measurements on all of them.

:book: Read the documentation :eyes:

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By optimizing ABR-ladders for specific content, you will make sure to not have wasteful rungs and this has been shown to cut bandwidth usage in half.

Usage

Transcoding and VMAF analysis can either be run in AWS or locally. When running in aws, you will need a running ECS cluster with a task definition configured to run easyvmaf-s3.

Installation

npm install --save @eyevinn/autovmaf

Environment Variables

A few environment variables can be set. These are:

LOAD_CREDENTIALS_FROM_ENV=true   //Load AWS credentials from environment variables
AWS_REGION=eu-north-1
AWS_ACCESS_KEY_ID=ABCD...
AWS_SECRET_ACCESS_KEY=EFGH...

Generate VMAF measurements

To generate VMAF measurements, you will need to define a job which can be created with the createJob()-function.

  const { createJob } = require('@eyevinn/autovmaf');
 
  const vmafScores = await createJob({
    name: "MyVMAFmeasurements",
    pipeline: "pipeline.yml",
    encodingProfile: "profile.json",
    reference: "reference.mp4",
    models: ["HD", "PhoneHD"],         // optional
    resolutions: [{                    // optional
      width: 1280,      
      height: 720, 
      range:                           // optional
         { min: 500000, 
         max: 600000 } 
   }],                                                
    bitrates: [500000, 600000],        // optional
    method: "bruteForce"               // optional
  });

When creating a job, you can specify:

• Name
  • This will name the folder in which to put the files.
• Pipeline
  • Path to a YAML-file that defines the pipeline. See examples/pipeline.yml for an example AWS-pipeline. Currently only AWS is supported.
• Encoding Profile
  • Path to a JSON-file that defines how the reference should be encoded. When using AWS, this is a MediaConvert configuration. For local pipelines, this is key-value pairs that will be passed as command line arguments to FFmpeg. See an example for AWS at examples/encoding-profile.json.
• Reference
  • Path to the reference video to analyze. Normally a local path, but when using AWS, this can also be an S3-URI.
• Models (optional)
  • A list of VMAF-models to use in evaluation. This can be HD, MobileHD and UHD. HD by default.
• Resolutions (optional)
  • A list of resolutions to test. By default it will test all resolutions in the example ABR-ladder provided by Apple in the HLS Authoring Spec.
  • Range (optional)
    • A min and max bitrate for testing a specific resolution. Adding a range will filter out bitrates that are outside of the given range. It is disabled by default.
• Bitrates (optional)
  • A list of bitrates to test. By default a list of bitrates between 150 kbit/s to 9000 kbit/s.
• Method (optional)
  • The method to use when analyzing the videos. Either bruteForce or walkTheHull. By default bruteForce. NOTE: walkTheHull is not implemented at the moment.

Create job using yaml

const { createJob } = require('@eyevinn/autovmaf');
const YAML = require('yaml');
const fs = require('fs');
const parseResolutions = resolutions => {
resolutions.map(resolutionStr => ({
   width: parseInt(resolutionStr.split('x')[0]),
   height: parseInt(resolutionStr.split('x')[1]),
  }));
};
const jobFile = fs.readFileSync('job.yml', 'utf-8');
const jobData = YAML.parse(jobFile);
const job = {
  ...jobData,
  resolutions: jobData['resolutions'] !== undefined ? parseResolutions(jobData['resolutions']) : undefined,
};
createJob(job);

An example of creating a job from a YAML-file can be seen in the examples-folder.

Read VMAF-scores

Using getVmaf(), you can read VMAF-scores from a JSON-file or a directory of JSON-files. This works on both local paths as well as S3-URIs with a "s3://"-prefix.

Example:

const vmafFiles = await getVmaf('s3://path/to/vmaf/');

vmafFiles.forEach(file => {
  console.log(file.filename + ': ' + file.vmaf);
});

CLI Usage

When running with the cli, all transcoding and vmaf analysis will be run locally.

Requirements

• easyVmaf
• FFmpeg >= 5.0, compiled with libvmaf
• Python >= 3.0

Installation

Installing with npm -g will make the autovmaf command available in your path

npm install -g @eyevinn/autovmaf

Environments variables

• EASYVMAF_PATH - needs to point to the file easyVmaf.py from your easyVmaf installation.
• FFMPEG_PATH - only needs to be set if ffmpeg is not in your path.
• PYTHON_PATH - only needs to be set if python is not in yur path.

Command line options

Available command line options for the cli can be listed with the --help argument

autovmaf <source>

run autovmaf for videofile source

Positionals:
  source  SOURCEFILE                                                    [string]

Options:
  --help         Show help                                             [boolean]
  --version      Show version number                                   [boolean]
  --resolutions  List of resolutions, ie 1920x1080,1280x720...          [string]
  --bitrates     List of bitrates, ie 800k,1000k,...                    [string]
  --name         Name for this autovmaf run
                                        [string] [default: "MyVMAFMeasurements"]
  --models       List of VMAF Models to use             [string] [default: "HD"]

Output files will be stored in a folder corresponding to the argument given to the --name option. If resolutions and/or bitrates are not specified default values will be used, See above.

Generate VMAF measurements

autovmaf --resolutions 1920x1080,1280x720,960x540 --bitrates 500k,800k,1200k,1600k,2000k,3000k,4000k --name my-autovmaf-test1 my-source-video.mp4

With the above command, when the run is finished transcoded files will be available in the folder my-autovmaf-test1, and vmaf-data in the folder my-autovmaf-test1/HD.

Development

Run tests

npm test

About Eyevinn Technology

Eyevinn Technology is an independent consultant firm specialized in video and streaming. Independent in a way that we are not commercially tied to any platform or technology vendor.

At Eyevinn, every software developer consultant has a dedicated budget reserved for open source development and contribution to the open source community. This give us room for innovation, team building and personal competence development. And also gives us as a company a way to contribute back to the open source community.

Want to know more about Eyevinn and how it is to work here. Contact us at [e-mail]!