ffmpeg-quality-metrics 3.1.4

Calculate video quality metrics with FFmpeg (SSIM, PSNR, VMAF)

FFmpeg Quality Metrics

Calculate various video quality metrics with FFmpeg.

Currently supports PSNR, SSIM, VMAF and VIF. It will output:

• the per-frame metrics
• metrics for each plane (Y, U, V) or components/submetrics (in the case of VIF, VMAF)
• global statistics (min/max/average/standard deviation)

Author: Werner Robitza [e-mail]

⚠️ BREAKING CHANGES: Version 3.0 adds the following changes:

• No more support for libvmaf 1.x
• Python ≥ 3.8 is required
• The global object of the JSON response now contains individual keys for each submetric, e.g. ["global"]["psnr"]["psnr_avg"]["average"].

Contents:

• Requirements
• Installation
• Usage
  • Metrics
  • Extended Options
  • Specifying VMAF Model
• Examples
• Running with Docker
• Output
  • JSON Output
  • CSV Output
• API
• Contributors
• License

---

Requirements

What you need:

• OS: Linux, macOS, Windows
• Python 3.8 or higher
• FFmpeg:
  • Linux: Download the git master build from here. Installation instructions, as well as how to add FFmpeg and FFprobe to your PATH, can be found here.
  • macOS: Download the snapshot build from here or install via brew install ffmpeg.
  • Windows: Download an FFmpeg binary from here. The git essentials build will suffice.

Put the ffmpeg executable in your $PATH.

If you want to calculate VMAF, your ffmpeg build should include libvmaf 2.3.1 or higher. This is the case with the static builds listed above or the Homebrew ffmpeg v5.1 package.

Installation

Using pip:

pip3 install ffmpeg-quality-metrics

Or clone this repository, then run the tool with python3 -m ffmpeg-quality-metrics.

Usage

In the simplest case, if you have a distorted (encoded, maybe scaled) version and the reference:

ffmpeg-quality-metrics distorted.mp4 reference.avi

The distorted file will be automatically scaled to the resolution of the reference, and the default metrics will be computed.

Metrics

The following metrics are available in this tool:

Metric	Description	Scale	Components/Submetrics	Calculated by default?
PSNR	Peak Signal to Noise Ratio	dB	mse_avg, mse_y, mse_u, mse_v, psnr_avg, psnr_y, psnr_u, psnr_v	✔️
SSIM	Structural Similarity	0-100 (higher is better)	ssim_y, ssim_u, ssim_v, ssim_avg	✔️
VMAF	Video Multi-Method Assessment Fusion	0-100 (higher is better)	vmaf, integer_adm2, integer_adm_scale0, integer_adm_scale1, integer_adm_scale2, integer_adm_scale3, integer_motion2, integer_motion, integer_vif_scale0, integer_vif_scale1, integer_vif_scale2, integer_vif_scale3	No
VIF	Visual Information Fidelity	0-100 (higher is better)	scale_0, scale_1, scale_2, scale_3	No

VMAF allows you to calculate even more additional features as submetrics:

Metric	Feature name	Core feature in VMAF?
PSNR	psnr
PSNR-HVS	psnr_hvs
CIEDE2000	ciede
CAMBI	cambi
VIF	vif	✔️
ADM	adm	✔️
Motion	motion	✔️
SSIM	float_ssim
MS-SSIM	float_ms_ssim

You can enable these with the --vmaf-features option (see usage below).

Extended Options

You can configure additional options related to scaling, speed etc.

See ffmpeg-quality-metrics -h:

usage: ffmpeg_quality_metrics [-h] [-n] [-v] [-p] [-k]
                              [-m {vmaf,psnr,ssim,vif} [{vmaf,psnr,ssim,vif} ...]]
                              [-s {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc,lanczos,spline}]
                              [-r FRAMERATE] [-t THREADS] [-of {json,csv}]
                              [--vmaf-model-path VMAF_MODEL_PATH]
                              [--vmaf-model-params VMAF_MODEL_PARAMS [VMAF_MODEL_PARAMS ...]]
                              [--vmaf-threads VMAF_THREADS] [--vmaf-subsample VMAF_SUBSAMPLE]
                              [--vmaf-features VMAF_FEATURES [VMAF_FEATURES ...]]
                              dist ref

ffmpeg_quality_metrics v3.0.0

positional arguments:
  dist                                  input file, distorted
  ref                                   input file, reference

options:
  -h, --help                            show this help message and exit

General options:
  -n, --dry-run                         Do not run commands, just show what would be done (default:
                                        False)
  -v, --verbose                         Show verbose output (default: False)
  -p, --progress                        Show a progress bar (default: False)
  -k, --keep-tmp                        Keep temporary files for debugging purposes (default: False)

Metric options:
  -m {vmaf,psnr,ssim,vif} [{vmaf,psnr,ssim,vif} ...], --metrics {vmaf,psnr,ssim,vif} [{vmaf,psnr,ssim,vif} ...]
                                        Metrics to calculate. Specify multiple metrics like '--
                                        metrics ssim vmaf' (default: ['psnr', 'ssim'])

FFmpeg options:
  -s {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc,lanczos,spline},
  --scaling-algorithm {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc,lanczos,spline}
                                        Scaling algorithm for ffmpeg (default: bicubic)
  -r FRAMERATE, --framerate FRAMERATE   Force an input framerate (default: None)
  -t THREADS, --threads THREADS         Number of threads to do the calculations (default: 0)

Output options:
  -of {json,csv}, --output-format {json,csv}
                                        Output format for the metrics (default: json)

VMAF options:
  --vmaf-model-path VMAF_MODEL_PATH     Use a specific VMAF model file. If none is chosen, picks a
                                        default model. You can also specify one of the following
                                        built-in models: ['vmaf_v0.6.1.json', 'vmaf_4k_v0.6.1.json',
                                        'vmaf_v0.6.1neg.json'] (default: /opt/homebrew/opt/libvmaf/s
                                        hare/libvmaf/model/vmaf_v0.6.1.json)
  --vmaf-model-params VMAF_MODEL_PARAMS [VMAF_MODEL_PARAMS ...]
                                        A list of params to pass to the VMAF model, specified as
                                        key=value. Specify multiple params like '--vmaf-model-params
                                        enable_transform=true enable_conf_interval=true' (default:
                                        None)
  --vmaf-threads VMAF_THREADS           Set the value of libvmaf's n_threads option. This determines
                                        the number of threads that are used for VMAF calculation.
                                        Set to 0 for auto. (default: 0)
  --vmaf-subsample VMAF_SUBSAMPLE       Set the value of libvmaf's n_subsample option. This is the
                                        subsampling interval, so set to 1 for default behavior.
                                        (default: 1)
  --vmaf-features VMAF_FEATURES [VMAF_FEATURES ...]
                                        A list of feature to enable. Pass the names of the features
                                        and any optional params. See https://github.com/Netflix/vmaf
                                        /blob/master/resource/doc/features.md for a list of
                                        available features. Params must be specified as 'key=value'.
                                        Multiple params must be separated by ':'. Specify multiple
                                        features like '--vmaf-features cambi:full_ref=true ciede'
                                        (default: None)

Specifying VMAF Model

Use the --vmaf-model-path option to set the path to a different VMAF model file. The default is vmaf_v0.6.1.json.

libvmaf version 2.x supports JSON-based model files only. This program has built-in support for the following models:

vmaf_v0.6.1.json
vmaf_4k_v0.6.1.json
vmaf_v0.6.1neg.json

Use the 4k version if you have a 4K reference sample. The neg version is explained here.

You can either specify an absolute path to an existing model, e.g.:

/usr/local/opt/libvmaf/share/model/vmaf_v0.6.1neg.json

Or pass the file name to the built-in model. So all of these work:

# use a downloaded JSON model for libvmaf 2.x
ffmpeg-quality-metrics dist.mkv ref.mkv -m vmaf --vmaf-model-path vmaf_v0.6.1neg.json

# use a different path for models on your system
ffmpeg-quality-metrics dist.mkv ref.mkv -m vmaf --vmaf-model-path /usr/local/opt/libvmaf/share/model/vmaf_v0.6.1neg.json

Examples

Run PSNR, SSIM, VMAF and VIF at the same time:

ffmpeg-quality-metrics dist.mkv ref.mkv \
    -m psnr ssim vmaf vif

Run VMAF with all the features:

ffmpeg-quality-metrics dist.mkv ref.mkv \
    -m vmaf \
    --vmaf-features ciede cambi psnr psnr_hvs motion adm vif

Enable feature options for CAMBI full-reference calculation:

ffmpeg-quality-metrics dist.mkv ref.mkv \
    -m vmaf \
    --vmaf-features cambi:full_ref=true

Running with Docker

If you don't want to deal with dependencies, build the image with Docker:

docker build -t ffmpeg_quality_metrics .

This takes a few minutes and installs the latest ffmpeg as a static build.

You can then run the container, which basically calls the Python script. To help you with mounting the volumes (since your videos are not stored in the container), you can run a helper script:

./docker_run.sh <dist> <ref> [OPTIONS]

Check the output of ./docker_run.sh for more help.

For example, to run the tool with the bundled test videos and enable VMAF calculation:

./docker_run.sh test/dist-854x480.mkv test/ref-1280x720.mkv -m vmaf

Output

This tool supports JSON or CSV output, including individual fields for planes/components/submetrics, and global statistics, as well as frame numbers (n).

JSON Output

The JSON output will include a key for each metric, and the value will be a list of values for each frame. Each frame is a dictionary with individual metrics per frame.

For instance, PSNR and SSIM output averages as well as per-component metrics. VMAF outputs different metrics depending on the enabled features.

The global key contains global statistics for each metric and its submetrics.

See the example.json file for an example of the output.

CSV Output

CSV output is using the tidy data principle, using one column per feature and one line per frame (observation).

Example:

n,adm2,motion2,ms_ssim,psnr,ssim,vif_scale0,vif_scale1,vif_scale2,vif_scale3,vmaf,mse_avg,mse_u,mse_v,mse_y,psnr_avg,psnr_u,psnr_v,psnr_y,ssim_avg,ssim_u,ssim_v,ssim_y,input_file_dist,input_file_ref
1,0.70704,0.0,0.89698,18.58731,0.92415,0.53962,0.71805,0.75205,0.77367,15.44212,536.71,234.48,475.43,900.22,20.83,24.43,21.36,18.59,0.945,0.96,0.942,0.934,test/dist-854x480.mkv,test/ref-1280x720.mkv
2,0.7064,0.35975,0.89806,18.60299,0.9247,0.54025,0.71961,0.75369,0.77607,15.85038,535.29,239.4,469.49,896.98,20.84,24.34,21.41,18.6,0.946,0.96,0.943,0.934,test/dist-854x480.mkv,test/ref-1280x720.mkv
3,0.70505,0.35975,0.89879,18.6131,0.92466,0.5391,0.71869,0.75344,0.77616,15.63546,535.04,245.8,464.43,894.89,20.85,24.22,21.46,18.61,0.945,0.959,0.943,0.934,test/dist-854x480.mkv,test/ref-1280x720.mkv

As there is no tidy way to represent global data in the same CSV file, you can use other tools to aggregate the data.

API

The program exposes an API that you can use yourself:

from ffmpeg_quality_metrics import FfmpegQualityMetrics as ffqm

ffqm("path/to/ref", "path/to/dist").calculate(["ssim", "psnr"])

For more usage please read the docs.

Contributors

Orkun Koçyiğit 💻

Hamas Shafiq 💻

Chris Griffith 💻

Ignacio Peletier 💻

License

ffmpeg-quality-metrics, Copyright (c) 2019-2022 Werner Robitza

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

For VMAF models, see ffmpeg_quality_metrics/vmaf_models/LICENSE.