Nnedi3ocl/nnedi3ocl

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Syntax and Parameters

nnedi3ocl (clip, int "field", bool "dh", bool "Y", bool "U", bool "V", int "nsize", int "nns", int "qual", int "etype", int "dw")

clip   =

Input clip must be planar.

int  field = -1

Controls the mode of operation (double vs same rate) and which field is kept. Possible settings:

• -2 = double rate (alternates each frame), uses avisynth's internal parity value to start
• -1 = same rate, uses avisynth's internal parity value
• 0 = same rate, keep bottom field
• 1 = same rate, keep top field
• 2 = double rate (alternates each frame), starts with bottom
• 3 = double rate (alternates each frame), starts with top

If field is set to -1, then nnedi3ocl calls child->GetParity(0) during initialization. If it returns true, then field is set to 1. If it returns false, then field is set to 0. If field is set to -2, then the same thing happens, but instead of setting field to 1 or 0 it is set to 3 or 2.

• Default for field is the value of dw.

bool  dh = false

Doubles the height of the input. Each line of the input is copied to every other line of the output and the missing lines are interpolated. If field=0, the input is copied to the odd lines of the output. If field=1, the input is copied to the even lines of the output.

• field must be set to either -1, 0, or 1 when using dh=true. • Default for dh is false when dw=-1 and true otherwise.

bool  Y = true

bool  U = true

bool  V = true

These control whether or not the specified plane is processed. Set to true to process or false to ignore. Ignored planes are not copied, zero'd, or even considered. So what the ignored planes happen to contain on output is unpredictable.

int  nsize = 0

Sets the size of the local neighborhood around each pixel that is used by the predictor neural network.

• 0 - 8x6

Only nsize 0 is implemented, other values are simply ignored.

int  nns = 1

Sets the number of neurons in the predictor neural network. Possible settings are 0, 1, 2, 3, and 4. 0 is fastest. 4 is slowest, but should give the best quality. This is a quality vs speed option; however, differences are usually small. The difference in speed will become larger as 'qual' is increased.

• 0 - 16
• 1 - 32
• 2 - 64
• 3 - 128
• 4 - 256

int  qual = 1

Controls the number of different neural network predictions that are blended together to compute the final output value. Each neural network was trained on a different set of training data. Blending the results of these different networks improves generalization to unseen data. Possible values are 1 or 2. Essentially this is a quality vs speed option. Larger values will result in more processing time, but should give better results. However, the difference is usually pretty small. I would recommend using qual>1 for things like single image enlargement.

int  etype = 0

Controls which set of weights to use in the predictor nn. Possible settings:

• 0 - weights trained to minimize absolute error
• 1 - weights trained to minimize squared error

int  dw = -1

Controls scaling in horizontal direction:

• -1 - no scaling.
• 0 - scales like field 0 with dh=true, but horizontally.
• 1 - scales like field 1 with dh=true, but horizontally.