Simple script for calculating quality metrics with FFmpeg.
Currently supports PSNR, SSIM, VMAF and VIF. It will output:
- the per-frame metrics
- metrics for each component (Y, U, V)
- global statistics (min/max/average/standard deviation)
Author: Werner Robitza [email protected]
Contents:
- Python 3.6 or higher
- FFmpeg:
Put the ffmpeg executable in your $PATH.
pip3 install ffmpeg_quality_metrics
Or clone this repository, then run the tool with python3 -m ffmpeg_quality_metrics
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.
The following metrics are available:
| Metric | Description | Scale | Calculated by default? |
|---|---|---|---|
| PSNR | Peak Signal to Noise Ratio | dB | ✔️ |
| SSIM | Structural Similarity | 0-100 (higher is better) | ✔️ |
| VMAF | Video Multi-Method Assessment Fusion | 0-100 (higher is better) | No |
| VIF | Visual Information Fidelity | 0-100 (higher is better) | No |
You can configure additional options related to scaling, speed etc.
See ffmpeg_quality_metrics -h:
usage: ffmpeg_quality_metrics [-h] [-n] [-k] [-v] [-p]
[-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}]
[--model-path MODEL_PATH] [--phone-model]
[--n-threads N_THREADS]
dist ref
positional arguments:
dist input file, distorted
ref input file, reference
optional arguments:
-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:
--model-path 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_4k_v0.6.1.json', 'vmaf_v0.6.1neg.pkl', 'vmaf_v0.6.1neg.json', 'vmaf_4k_v0.6.1.pkl', 'vmaf_v0.6.1.pkl', 'vmaf_v0.6.1.json'] (default:
/usr/local/share/model/vmaf_v0.6.1.pkl)
--phone-model Enable VMAF phone model (default: False)
--n-threads N_THREADS
Set the value of libvmaf's n_threads option. This
determines the number of threads that are used for
VMAF calculation (default: 8)
Use the --model-path option to set the path to a different VMAF model file.
libvmaf version 2.x supports JSON-based model files, whereas libvmaf version 1.x only supports .pkl files. Depending on your version of libvmaf, you need to specify a different model name or path. If you get an error like "libvmaf encountered an error, check log for details", this might be the problem.
This program has built-in support for the following models:
vmaf_v0.6.1.pkl
vmaf_v0.6.1.json
vmaf_4k_v0.6.1.pkl
vmaf_4k_v0.6.1.json
vmaf_v0.6.1neg.pkl
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 --model-path vmaf_v0.6.1neg.json
# use the models that came with a static build, if you installed them to /usr/local/share/model as instructed
ffmpeg_quality_metrics dist.mkv ref.mkv -m vmaf --model-path /usr/local/share/model/vmaf_v0.6.1.pkl
# use a different path for models on your system
ffmpeg_quality_metrics dist.mkv ref.mkv -m vmaf --model-path /usr/local/opt/libvmaf/share/model/vmaf_v0.6.1neg.jsonIf 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 -ev
For Homebrew ffmpeg, a Dockerfile-legacy is provided.
This tool supports JSON or CSV output, including individual fields for Y, U, V, and global statistics, as well as frame numbers (n).
JSON example:
➜ ffmpeg_quality_metrics test/dist-854x480.mkv test/ref-1280x720.mkv -m ssim psnr vmaf
{
"ssim": [
{
"n": 1,
"ssim_y": 0.934,
"ssim_u": 0.96,
"ssim_v": 0.942,
"ssim_avg": 0.945
},
{
"n": 2,
"ssim_y": 0.934,
"ssim_u": 0.96,
"ssim_v": 0.943,
"ssim_avg": 0.946
},
{
"n": 3,
"ssim_y": 0.934,
"ssim_u": 0.959,
"ssim_v": 0.943,
"ssim_avg": 0.945
}
],
"psnr": [
{
"n": 1,
"mse_avg": 536.71,
"mse_y": 900.22,
"mse_u": 234.48,
"mse_v": 475.43,
"psnr_avg": 20.83,
"psnr_y": 18.59,
"psnr_u": 24.43,
"psnr_v": 21.36
},
{
"n": 2,
"mse_avg": 535.29,
"mse_y": 896.98,
"mse_u": 239.4,
"mse_v": 469.49,
"psnr_avg": 20.84,
"psnr_y": 18.6,
"psnr_u": 24.34,
"psnr_v": 21.41
},
{
"n": 3,
"mse_avg": 535.04,
"mse_y": 894.89,
"mse_u": 245.8,
"mse_v": 464.43,
"psnr_avg": 20.85,
"psnr_y": 18.61,
"psnr_u": 24.22,
"psnr_v": 21.46
}
],
"vmaf": [
{
"psnr": 18.587308,
"integer_motion2": 0.0,
"integer_motion": 0.0,
"integer_adm2": 0.69907,
"integer_adm_scale0": 0.708183,
"integer_adm_scale1": 0.733469,
"integer_adm_scale2": 0.718624,
"integer_adm_scale3": 0.67301,
"ssim": 0.925976,
"integer_vif_scale0": 0.539591,
"integer_vif_scale1": 0.718022,
"integer_vif_scale2": 0.751875,
"integer_vif_scale3": 0.773503,
"ms_ssim": 0.898265,
"vmaf": 14.054853,
"n": 1
},
{
"psnr": 18.60299,
"integer_motion2": 0.359752,
"integer_motion": 0.368929,
"integer_adm2": 0.698451,
"integer_adm_scale0": 0.706706,
"integer_adm_scale1": 0.73203,
"integer_adm_scale2": 0.718262,
"integer_adm_scale3": 0.672766,
"ssim": 0.926521,
"integer_vif_scale0": 0.540231,
"integer_vif_scale1": 0.719566,
"integer_vif_scale2": 0.753567,
"integer_vif_scale3": 0.775864,
"ms_ssim": 0.899353,
"vmaf": 14.464182,
"n": 2
},
{
"psnr": 18.613101,
"integer_motion2": 0.359752,
"integer_motion": 0.359752,
"integer_adm2": 0.697126,
"integer_adm_scale0": 0.706542,
"integer_adm_scale1": 0.731351,
"integer_adm_scale2": 0.716454,
"integer_adm_scale3": 0.671197,
"ssim": 0.926481,
"integer_vif_scale0": 0.539091,
"integer_vif_scale1": 0.718657,
"integer_vif_scale2": 0.753306,
"integer_vif_scale3": 0.775984,
"ms_ssim": 0.900086,
"vmaf": 14.256442,
"n": 3
}
],
"global": {
"ssim": {
"average": 0.945,
"median": 0.945,
"stdev": 0.0,
"min": 0.945,
"max": 0.946
},
"psnr": {
"average": 20.84,
"median": 20.84,
"stdev": 0.008,
"min": 20.83,
"max": 20.85
},
"vmaf": {
"average": 14.258,
"median": 14.256,
"stdev": 0.167,
"min": 14.055,
"max": 14.464
}
},
"input_file_dist": "test/dist-854x480.mkv",
"input_file_ref": "test/ref-1280x720.mkv"
}
CSV example:
➜ ffmpeg_quality_metrics test/dist-854x480.mkv test/ref-1280x720.mkv --enable-vmaf -of csv
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
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").calc(["ssim", "psnr"])For more usage please read the docs.
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.
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