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doc/filters: Add entry for sr filter.
Signed-off-by: Gyan Doshi <ffmpeg@gyani.pro>
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@ -15403,6 +15403,65 @@ option may cause flicker since the B-Frames have often larger QP. Default is
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@code{0} (not enabled).
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@end table
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@section sr
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Scale the input by applying one of the super-resolution methods based on
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convolutional neural networks.
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Training scripts as well as scripts for model generation are provided in
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the repository at @url{https://github.com/HighVoltageRocknRoll/sr.git}.
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The filter accepts the following options:
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@table @option
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@item model
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Specify which super-resolution model to use. This option accepts the following values:
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@table @samp
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@item srcnn
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Super-Resolution Convolutional Neural Network model.
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See @url{https://arxiv.org/abs/1501.00092}.
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@item espcn
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Efficient Sub-Pixel Convolutional Neural Network model.
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See @url{https://arxiv.org/abs/1609.05158}.
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@end table
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Default value is @samp{srcnn}.
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@item dnn_backend
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Specify which DNN backend to use for model loading and execution. This option accepts
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the following values:
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@table @samp
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@item native
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Native implementation of DNN loading and execution.
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@item tensorflow
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TensorFlow backend. To enable this backend you
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need to install the TensorFlow for C library (see
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@url{https://www.tensorflow.org/install/install_c}) and configure FFmpeg with
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@code{--enable-libtensorflow}
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@end table
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Default value is @samp{native}.
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@item scale_factor
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Set scale factor for SRCNN model, for which custom model file was provided.
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Allowed values are @code{2}, @code{3} and @code{4}. Default value is @code{2}.
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Scale factor is necessary for SRCNN model, because it accepts input upscaled
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using bicubic upscaling with proper scale factor.
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@item model_filename
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Set path to model file specifying network architecture and its parameters.
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Note that different backends use different file formats. TensorFlow backend
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can load files for both formats, while native backend can load files for only
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its format.
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@end table
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@anchor{subtitles}
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@section subtitles
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