@eyevinn/autovmaf

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

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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: [
    // optional
    500000, 600000, 800000
  ],
  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.
  • When running locally, pipeline data can be inlined in the job definition.
• Encoding Profile
  • Path to a JSON-file that defines how the reference should be encoded. When using AWS, this is a MediaConvert configuration. See an example for AWS at examples/aws/encoding-profile.json. For local pipelines, this is key-value pairs that will be passed as command line arguments to FFmpeg. If pipeline data is inlined in the job definition, encodingProfile can be omitted and key-value pairs can instead be set in the ffmpegOptions property of the pipeline object.
• 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

Global installation

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

npm install -g @eyevinn/autovmaf

Environments variables

These are only needed if you are running transcodes and VMAF measurements locally

• 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 your path.

Command line options

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

autovmaf [source]

run transcode and vmaf analysis for videofile source

Commands:
  autovmaf [source]                 run transcode and vmaf analysis for
                                    videofile source                   [default]
  autovmaf suggest-ladder <folder>  Suggest bitrate ladder given vmaf results
  autovmaf export-csv <folder>      Export Vmaf results as csv

Positionals:
  source  SOURCEFILE                                                    [string]

Options:
  --version         Show version number                                [boolean]
  --help            Show help                                          [boolean]
  --resolutions     List of resolutions, ie 1920x1080,1280x720...       [string]
  --bitrates        List of bitrates, ie 800k,1000k,...                 [string]
  --name            Name for this autovmaf run                          [string]
  --models          List of VMAF Models to use                          [string]
  --job             File with job definition                            [string]
  --saveAsCsv       Save VMAF measurements as a .csv file in addition to a JSON
                    file                              [boolean] [default: false]
  --skipTranscode   Skip transcode and run vmaf on already transcoded files
                                                      [boolean] [default: false]
  --skipExisting    Skip transcode for already transcoded files
                                                       [boolean] [default: true]
  --probeBitrate    Read bitrate of transcoded file with ffprobe
                                                      [boolean] [default: false]
  --ffmpeg-options  List of options to pass to ffmpeg, on the form
                    key1=value1:key2=value2                             [string]

Running transcode and analysis

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.

Providing job definition in a json or yaml file

With the --job option, a path to a yaml or json file with a job definition can be passed to to the cli. The values defined in the file can be overridden with other commandline options. For instance the reference video defined in the job file can be overridden by passing a source file on the command line.

Using variables in the job definition

It is possible to iterate over other variables than bitrate and resolutions when running a local encode. For instance, to run transcode and vmaf analysis with x265 in CRF mode for a number of CRF values, a job definition like below can be used (also available in examples/local/local-job-crf.yaml)

models:
  - HD
resolutions:
  - width: 1920
    height: 1080
bitrates:
  - 0
pipeline:
  ffmpegEncoder: libx265
  singlePass: true
  skipDefaultOptions: true
  ffmpegOptions:
    '-pix_fmt': 'yuv420p'
    '-preset': 'veryslow'
    '-x265-params': 'crf=${CRF}:scenecut=0:keyint=50:min-keyint=50:open-gop=0'
  easyVmafExtraArgs:
    '-threads': 20
pipelineVariables:
  CRF:
    - 22
    - 26
    - 30
    - 34

This will run transcode and vmaf analysis for CRF values 22,26,30, and 34. Variables are used in the ffmpeg options by inserting ${VARIABLENAME}. This string will then be substituted with a value from the list of values from pipelineVariables.VARIABLENAME. Note that when running CRF encode or other non-ABR mode, skipDefaultOptions must be set to avoid injecting bitrate options to ffmpeg. Also note that the cli needs to be run with the --probe-bitrate option to get the correct bitrate from the transcoded files.

It is also possible to use pipelineVariables with the AWSPipeline. The following example will run transcode and vmaf analysis using the AWS MediaCOnvert QVBR levels 6, 7, 8 and 9.

name: job-name
pipeline: pipeline.yml
encodingProfile: encoding-profile.json
reference: reference.mp4
models:
  - HD
resolutions:
  - width: 1920
    height: 1080
bitrates:
  - 0
pipelineVariables:
  QVBR:
    - 6
    - 7
    - 8
    - 9

Generate VMAF measurements example

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.

Exporting results to csv

To export results to csv, use the export-csv command.

autovmaf export-csv <folder>

Export Vmaf results as csv

Positionals:
  folder  Folder with vmaf measurement results               [string] [required]

Options:
  --version       Show version number                                  [boolean]
  --help          Show help                                            [boolean]
  --probeBitrate  Read bitrate of transcoded file with ffprobe
                                                      [boolean] [default: false]
  --variables     List of variables to include as columns in csv        [string]

If your job uses variables (See above), the variables that should be included in the csv data should be specified with the --variables option.

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]!