Nnedi3/nnedi3 rpow2

Back to nnedi3 ←

---

Description

nnedi3 is an intra-field only deinterlacer. It takes in a frame, throws away one field, and then interpolates the missing pixels using only information from the kept field. It has same rate and double rate modes, and works with YV12, YUY2, and RGB24 input.

Syntax and Parameters

nnedi3_rpow2 (clip, int rfactor, int "nsize", int "nns", int "qual", int "etype", int "pscrn", string "cshift", int "fwidth", int "fheight", float "ep0", float "ep1", int "threads", int "opt", int "fapprox")

int  rfactor =

Image enlargement factor. Must be a power of 2 in the range [2,1024].

int  nsize = 0

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

neural network. Possible settings (x_diameter x y_diameter):

• 0 - 8x6
• 1 - 16x6
• 2 - 32x6
• 3 - 48x6
• 4 - 8x4
• 5 - 16x4
• 6 - 32x4

For image enlargement it is recommended to use 0 or 4. Larger y_diameter settings

will result in sharper output. For deinterlacing larger x_diameter settings will

allow connecting lines of smaller slope. However, what setting to use really

depends on the amount of aliasing (lost information) in the source. If the source was

heavily low-pass filtered before interlacing then aliasing will be low and a large

x_diameter setting wont be needed, and vice versa.

int  nns = 3

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  pscrn = 2

Controls whether or not the prescreener neural network is used to decide which pixels

should be processed by the predictor neural network and which can be handled by simple

cubic interpolation. The prescreener is trained to know whether cubic interpolation

will be sufficient for a pixel or whether it should be predicted by the predictor nn.

The computational complexity of the prescreener nn is much less than that of the predictor

nn. Since most pixels can be handled by cubic interpolation, using the prescreener

generally results in much faster processing. The prescreener is pretty accurate, so the

difference between using it and not using it is almost always unnoticeable.

Version 0.9.3 adds a new, faster prescreener with three selectable 'levels', which

trade off the number of pixels detected as only requiring cubic interpolation versus

incurred error. Therefore, pscrn is now an integer with possible values of 0, 1, 2, 3,

and 4.

• 0 - no prescreening (same as false in prior versions)
• 1 - original prescreener (same as true in prior versions)
• 2 - new prescreener level 0
• 3 - new prescreener level 1
• 4 - new prescreener level 2

*Higher levels for the new prescreener result in cubic interpolation being

used on fewer pixels (so are slower, but incur less error). However, the

difference is pretty much unnoticable. Level 2 is closest to the original

prescreener in terms of incurred error, but is much faster.

string  cshift =

Sets the resizer used for correcting the image center shift that nnedi3_rpow2

introduces. This can be any of Avisynth's internal resizers, such as "spline36resize",

"lanczosresize", etc... If not specified the shift is not corrected. The correction

is accomplished by using the subpixel cropping capability of Avisynth's internal

resizers.

int  fwidth =

int  fheight =

If correcting the image center shift by using the 'cshift' parameter, fwidth/fheight

allow you to set a new output resolution. First, the image is enlarged by 'rfactor'

using nnedi3. Once that is completed the image center shift is corrected, and the

image is resampled to fwidth x fheight resolution. The shifting and resampling

happen in one call using the internal Avisynth resizer you specify via the 'cshift'

string. If fwidth/fheight are not specified, then they are set equal to rfactor*width

and rfactor*height respectively (in other words they do nothing).

float  ep0 =

float  ep1 =

Some Avisynth resizers take optional arguments, such as 'taps' for lanczosresize or

'p' for gaussresize. ep0/ep1 allow you to pass values for these optional arguments

when using the 'cshift' parameter. If the resizer only takes one optional argument

then ep0 is used. If the argument that the resizer takes is not a float value,

then ep0 gets rounded to an integer. If the resizer takes two optional arguments,

then ep0 corresponds to the first one, and ep1 corresponds to the second. The only

resizer that takes more than one optional argument is bicubicresize(), which takes

'b' and 'c'. So ep0 = b, and ep1 = c. If ep0/ep1 are not set then the default value

for the optional argument(s) of the resizer is used.

int  threads = 0

Controls how many threads will be used for processing. If set to 0, threads will

be set equal to the number of detected processors.

int  opt = 0

Sets which cpu optimizations to use. Possible settings:

• 0 = auto detect
• 1 = use c
• 2 = use sse2

int  fapprox = 15

Bitmask which enables or disables certain speed-ups. Value range is [0,15].

Mainly for debugging.

• 0 = nothing
• &1 = use int16 dot products in first layer of prescreener nn
• &2 = use int16 dot products in predictor nn
• &12 = 4 = use exp function approximation in predictor nn
• &12 = 8|12 = use faster (and more inaccurate) exp function approximation in predictor nn

Examples

• Enlarge image by 4x with default settings (identical to the 2nd example):

AviSource("Blah.avi")
nnedi3_rpow2(rfactor=4, nsize=0, nns=3, qual=1, etype=0, pscrn=2, threads=0, opt=0, fapprox=15)

• Enlarge image by 4x, don't correct for center shift.

AviSource("Blah.avi")
nnedi3_rpow2(rfactor=4)

• Enlarge image by 2x, correct for center shift using Spline36Resize.

AviSource("Blah.avi")
nnedi3_rpow2(rfactor=2,cshift="Spline36Resize")

• Enlarge image by 8x, correct for center shift and downsample from 8x to 7x using LanczosResize with 5 taps.

AviSource("Blah.avi")
nnedi3_rpow2(rfactor=8,cshift="LanczosResize",fwidth=width*7,fheight=height*7,ep0=5)

---

Back to nnedi3 ←