doc/filters: Add entry for sr filter.

Signed-off-by: Gyan Doshi <ffmpeg@gyani.pro>

1 files changed, 59 insertions, 0 deletions

diff --git a/doc/filters.texi b/doc/filters.texi
index 267bd04a43..32c95b591c 100644
--- a/doc/filters.texi
+++ b/doc/filters.texi
@@ -15403,6 +15403,65 @@ option may cause flicker since the B-Frames have often larger QP. Default is
 @code{0} (not enabled).
 @end table
 
+@section sr
+
+Scale the input by applying one of the super-resolution methods based on
+convolutional neural networks.
+
+Training scripts as well as scripts for model generation are provided in
+the repository at @url{https://github.com/HighVoltageRocknRoll/sr.git}.
+
+The filter accepts the following options:
+
+@table @option
+@item model
+Specify which super-resolution model to use. This option accepts the following values:
+
+@table @samp
+@item srcnn
+Super-Resolution Convolutional Neural Network model.
+See @url{https://arxiv.org/abs/1501.00092}.
+
+@item espcn
+Efficient Sub-Pixel Convolutional Neural Network model.
+See @url{https://arxiv.org/abs/1609.05158}.
+
+@end table
+
+Default value is @samp{srcnn}.
+
+@item dnn_backend
+Specify which DNN backend to use for model loading and execution. This option accepts
+the following values:
+
+@table @samp
+@item native
+Native implementation of DNN loading and execution.
+
+@item tensorflow
+TensorFlow backend. To enable this backend you
+need to install the TensorFlow for C library (see
+@url{https://www.tensorflow.org/install/install_c}) and configure FFmpeg with
+@code{--enable-libtensorflow}
+
+@end table
+
+Default value is @samp{native}.
+
+@item scale_factor
+Set scale factor for SRCNN model, for which custom model file was provided.
+Allowed values are @code{2}, @code{3} and @code{4}. Default value is @code{2}.
+Scale factor is necessary for SRCNN model, because it accepts input upscaled
+using bicubic upscaling with proper scale factor.
+
+@item model_filename
+Set path to model file specifying network architecture and its parameters.
+Note that different backends use different file formats. TensorFlow backend
+can load files for both formats, while native backend can load files for only
+its format.
+
+@end table
+
 @anchor{subtitles}
 @section subtitles