ffmpeg 增加视频流媒体质量评估滤镜 (Video Multimethod Assessment Fusion, VMAF)

URL: https://github.com/Netflix/vmaf

libvmaf

Obtain the VMAF (Video Multi-Method Assessment Fusion) score between two input videos.

The obtained VMAF score is printed through the logging system.

It requires Netflix’s vmaf library (libvmaf) as a pre-requisite. After installing the library it can be enabled using: ./configure --enable-libvmaf. If no model path is specified it uses the default model: vmaf_v0.6.1.pkl.

The filter has following options:

‘model_path’
Set the model path which is to be used for SVM. Default value: "vmaf_v0.6.1.pkl"

‘log_path’
Set the file path to be used to store logs.

‘log_fmt’
Set the format of the log file (xml or json).

‘enable_transform’
Enables transform for computing vmaf.

‘phone_model’
Invokes the phone model which will generate VMAF scores higher than in the regular model, which is more suitable for laptop, TV, etc. viewing conditions.

‘psnr’
Enables computing psnr along with vmaf.

‘ssim’
Enables computing ssim along with vmaf.

‘ms_ssim’
Enables computing ms_ssim along with vmaf.

‘pool’
Set the pool method (mean, min or harmonic mean) to be used for computing vmaf.

This filter also supports the framesync options.

On the below examples the input file ‘main.mpg’ being processed is compared with the reference file ‘ref.mpg’.

ffmpeg -i main.mpg -i ref.mpg -lavfi libvmaf -f null -
Example with options:

ffmpeg -i main.mpg -i ref.mpg -lavfi libvmaf="psnr=1:enable-transform=1" -f null -

vmafmotion
Obtain the average vmaf motion score of a video. It is one of the component filters of VMAF.

The obtained average motion score is printed through the logging system.

In the below example the input file ‘ref.mpg’ is being processed and score is computed.

ffmpeg -i ref.mpg -lavfi vmafmotion -f null -