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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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