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@ -9885,25 +9885,16 @@ distances from the focal point in the source and target images, respectively. |
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@section libvmaf |
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Obtain the average VMAF (Video Multi-Method Assessment Fusion) |
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Obtain the VMAF (Video Multi-Method Assessment Fusion) |
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score between two input videos. |
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This filter takes two input videos. |
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Both video inputs must have the same resolution and pixel format for |
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this filter to work correctly. Also it assumes that both inputs |
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have the same number of frames, which are compared one by one. |
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The obtained average VMAF score is printed through the logging system. |
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The obtained VMAF score is printed through the logging system. |
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It requires Netflix's vmaf library (libvmaf) as a pre-requisite. |
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After installing the library it can be enabled using: |
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@code{./configure --enable-libvmaf}. |
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If no model path is specified it uses the default model: @code{vmaf_v0.6.1.pkl}. |
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On the below examples the input file @file{main.mpg} being processed is |
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compared with the reference file @file{ref.mpg}. |
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The filter has following options: |
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@table @option |
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@ -9934,12 +9925,14 @@ Enables computing ssim along with vmaf. |
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Enables computing ms_ssim along with vmaf. |
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@item pool |
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Set the pool method to be used for computing vmaf. |
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Set the pool method (mean, min or harmonic mean) to be used for computing vmaf. |
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@end table |
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This filter also supports the @ref{framesync} options. |
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For example: |
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On the below examples the input file @file{main.mpg} being processed is |
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compared with the reference file @file{ref.mpg}. |
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@example |
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ffmpeg -i main.mpg -i ref.mpg -lavfi libvmaf -f null - |
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@end example |
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