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FFmpeg Quality Metrics

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PyPI version

Python package

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 werner.robitza@gmail.com

Note: Previous versions installed a ffmpeg_quality_metrics executable. To harmonize it with other tools, now the executable is called ffmpeg-quality-metrics. Please ensure you remove the old executable (e.g. run which ffmpeg_quality_metrics and remove the file).

Contents:


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 (PSNR, SSIM) will be computed.

Note that if your distorted file is not in time sync with the reference, you can use the --dist-delay option to delay the distorted file by a certain amount of seconds (positive or negative).

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

As shown in the table, every metric can have more than one submetric computed, and they will be printed in the output.

If you want to calculate additional metrics, enable them with the --metrics option:

ffmpeg-quality-metrics distorted.mp4 reference.avi --metrics psnr ssim vmaf

Specify multiple metrics by separating them with a space (e.g., in the above example, psnr ssim vmaf).

Here, VMAF uses the default model. You can specify a different model with the --vmaf-model option. VMAF also allows you to calculate even more additional features as submetrics. You can enable these with the --vmaf-features option.

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] [--tmp-dir TMP_DIR]
                              [-m {vmaf,psnr,ssim,vif} [{vmaf,psnr,ssim,vif} ...]]
                              [-s {fast_bilinear,bilinear,bicubic,experimental,neighbor,area,bicublin,gauss,sinc,lanczos,spline}]
                              [-r FRAMERATE] [--dist-delay DIST_DELAY] [-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 v3.2.1

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)
  --tmp-dir TMP_DIR                     Directory to store temporary files in (will use system
                                        default if not specified) (default: None)

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)
  --dist-delay DIST_DELAY               Delay the distorted video against the reference by this many
                                        seconds (default: 0.0)
  -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)

VMAF-specific Settings

As VMAF is more complex than the other metrics, it has a few more options.

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

Specifying VMAF Features

VMAF includes several metrics, each of which correspond to a feature name. By default, only three core features are used. Use the --vmaf-features option to enable additional features on top of the core features.

The following table shows the available features:

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

To find out more about the features, check out the VMAF documentation.

For example, to enable the CAMBI feature, use:

ffmpeg-quality-metrics dist.mkv ref.mkv -m vmaf --vmaf-features cambi

VMAF Feature Parameters

Some features additionally take a number of optional parameters. The following table shows the available parameters for each feature:

Feature Parameter Default Description
adm adm_csf_mode 0 Contrast sensitivity function
adm adm_enhn_gain_limit 100.0 Enhancement gain imposed on adm, must be >= 1.0, where 1.0 means the gain is completely disabled
adm adm_norm_view_dist 3.0 Normalized viewing distance = viewing distance / ref display's physical height
adm adm_ref_display_height 1080 Reference display height in pixels
adm debug false Debug mode: enable additional output
cambi enc_bitdepth Encoding bitdepth.
cambi enc_height Encoding height.
cambi enc_width Encoding width.
cambi eotf bt1886 Determines the EOTF used to compute the visibility thresholds.
cambi full_ref false Set to true to enable full-reference CAMBI calculation.
cambi heatmaps_path Set to a target folder where the CAMBI heatmaps will be stored as .gray files
cambi max_log_contrast 2 Maximum contrast in log luma level (2^max_log_contrast) at 10-bits.
cambi src_height Source height. Only used when full_ref=true.
cambi src_width Source width. Only used when full_ref=true.
cambi topk 0.2 Ratio of pixels for the spatial pooling computation.
cambi tvi_threshold 0.75 Visibility threshold for luminance ΔL < tvi_threshold*L_mean.
cambi window_size 63 Window size to compute CAMBI: 63 corresponds to ~1 degree at 4k.
motion debug true Enable additional output for debugging.
motion motion_force_zero false Force the motion score to be zero. This parameter is a feature-specific parameter.
ms_ssim clip_db false Clip dB scores
ms_ssim enable_db false Write MS-SSIM values as dB
ms_ssim enable_lcs false Enable luminance, contrast and structure intermediate output
ssim clip_db false Clip dB scores
ssim enable_db false Write SSIM values as dB
ssim enable_lcs false Enable luminance, contrast and structure intermediate output
vif debug false Debug mode: enable additional output
vif vif_enhn_gain_limit 100.0 Enhancement gain imposed on vif, must be >= 1.0, where 1.0 means the gain is completely disabled
vif vif_kernelscale 1.0 Scaling factor for the gaussian kernel (2.0 means multiplying the standard deviation by 2 and enlarge the kernel size accordingly)

The parameters are specified as key=value pairs, separated by :. For example, to enable the full-reference CAMBI calculation, use:

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

To generate the CAMBI heatmaps, use:

ffmpeg-quality-metrics dist.mkv ref.mkv -m vmaf --vmaf-features cambi:heatmaps_path=/tmp/cambi

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

ffqm = FfmpegQualityMetrics("path/to/reference-video.mp4", "path/to/distorted-video.mp4")

metrics = ffqm.calculate(["ssim", "psnr"])

# check the available metrics
print(metrics.keys())
# ['ssim', 'psnr']

# get the SSIM values for the first frame
print(metrics["ssim"][0])
# {'n': 1, 'ssim_y': 0.934, 'ssim_u': 0.96, 'ssim_v': 0.942, 'ssim_avg': 0.945}

# average the ssim_y values over all frames
print(sum([frame["ssim_y"] for frame in metrics["ssim"]]) / len(metrics["ssim"]))

# or just get the global stats
print(ffqm.get_global_stats()["ssim"]["ssim_y"]["average"])

For more usage please read the docs.

Contributors

Orkun Koçyiğit
Orkun Koçyiğit

💻
Hamas Shafiq
Hamas Shafiq

💻
Chris Griffith
Chris Griffith

💻
Ignacio Peletier
Ignacio Peletier

💻
Nav
Nav

🐛

License

ffmpeg-quality-metrics, Copyright (c) 2019-2023 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.