Manual: pamscale
Pamscale User Manual(1) General Commands Manual Pamscale User Manual(1)
NAME
pamscale - scale a Netpbm image
SYNOPSIS
pamscale
[
scale_factor
|
{-xyfit | -xyfill | -xysize}
cols rows
|
-reduce reduction_factor
|
[-xsize=cols | -width=cols | -xscale=factor]
[-ysize=rows | -height=rows | -yscale=factor]
|
-pixels n
]
[
-nomix
|
-filter=functionName [-window=functionName]
]
[-linear]
[-reportonly]
[-verbose]
[pnmfile]
Minimum unique abbreviation of option is acceptable. You may use double hyphens instead of single hyphen to denote
options. You may use white space in place of the equals sign to separate an option name from its value.
DESCRIPTION
This program is part of Netpbm(1).
pamscale scales a Netpbm image by a specified factor, or scales individually horizontally and vertically by specified
factors.
You can either enlarge (scale factor > 1) or reduce (scale factor < 1).
pamscale works on multi-image streams, scaling each one independently. But before Netpbm 10.49 (December 2009), it
scales only the first image and ignores the rest of the stream.
The Scale Factors
The options -width, -height, -xsize, -ysize, -xscale, -yscale, -xyfit, -xyfill, -reduce, and -pixels control the
amount of scaling. For backward compatibility, there are also -xysize and the scale_factor argument, but you
shouldn't use those.
-width and -height specify the width and height in pixels you want the resulting image to be. See below for rules
when you specify one and not the other.
-xsize and -ysize are synonyms for -width and -height, respectively.
-xscale and -yscale tell the factor by which you want the width and height of the image to change from source to reâ
sult (e.g. -xscale 2 means you want to double the width; -xscale .5 means you want to halve it). See below for
rules when you specify one and not the other.
When you specify an absolute size or scale factor for both dimensions, pamscale scales each dimension independently
without consideration of the aspect ratio.
If you specify one dimension as a pixel size and don't specify the other dimension, pamscale scales the unspecified
dimension to preserve the aspect ratio.
If you specify one dimension as a scale factor and don't specify the other dimension, pamscale leaves the unspecified
dimension unchanged from the input.
If you specify the scale_factor parameter instead of dimension options, that is the scale factor for both dimensions.
It is equivalent to -xscale=scale_factor -yscale=scale_factor.
Specifying the -reduce reduction_factor option is equivalent to specifying the scale_factor parameter, where
scale_factor is the reciprocal of reduction_factor.
-xyfit specifies a bounding box. pamscale scales the input image to the largest size that fits within the box, while
preserving its aspect ratio. -xysize is a synonym for this. Before Netpbm 10.20 (January 2004), -xyfit did not exâ
ist, but -xysize did.
-xyfill is similar, but pamscale scales the input image to the smallest size that completely fills the box, while
preserving its aspect ratio. This option has existed since Netpbm 10.20 (January 2004).
-pixels specifies a maximum total number of output pixels. pamscale scales the image down to that number of pixels.
If the input image is already no more than that many pixels, pamscale just copies it as output; pamscale does not
scale up with -pixels.
If you enlarge by a factor of 3 or more, you should probably add a pnmsmooth step; otherwise, you can see the origiâ
nal pixels in the resulting image.
-reportonly
The option -reportonly causes pamscale not to scale the image, but instead to report to Standard Output what scaling
the options and the input image dimensions indicate. For example, if you specify
-xyfill 100 100 -reportonly
and the input image is 500 x 400, pamscale tells you that this means scaling by .25 to end up with a 125 x 100 image.
You can use this information with other programs, such as pamscalefixed, that don't have as rich facilities as pamâ
scale for choosing scale factors.
The output is intended to be convenient for machine processing. In the example above, it would be
500 400 0.250000 0.250000 125 100
The output is a single line of text per input image, with blank-separated tokens as follows.
⢠input width in pixels, decimal unsigned integer
⢠input height in pixels, decimal unsigned integer
⢠horizontal scale factor, floating point decimal, unsigned
⢠vertical scale factor, floating point decimal, unsigned
⢠output width in pixels, decimal unsigned integer
⢠output height in pixels, decimal unsigned integer
-reportonly was new in Netpbm 10.86 (March 2019).
Usage Notes
A useful application of pamscale is to blur an image. Scale it down (without -nomix) to discard some information,
then scale it back up using pamstretch.
Or scale it back up with pamscale and create a "pixelized" image, which is sort of a computer-age version of blurâ
ring.
Transparency
pamscale understands transparency and properly mixes pixels considering the pixels' transparency.
Proper mixing does not mean just mixing the transparency value and the color component values separately. In a PAM
image, a pixel which is not opaque represents a color that contains light of the foreground color indicated explicâ
itly in the PAM and light of a background color to be named later. But the numerical scale of a color component samâ
ple in a PAM is as if the pixel is opaque. So a pixel that is supposed to contain half-strength red light for the
foreground plus some light from the background has a red color sample that says full red and a transparency sample
that says 50% opaque. In order to mix pixels, you have to first convert the color sample values to numbers that repâ
resent amount of light directly (i.e. multiply by the opaqueness) and after mixing, convert back (divide by the
opaqueness).
Input And Output Image Types
pamscale produces output of the same type (and tuple type if the type is PAM) as the input, except if the input is
PBM. In that case, the output is PGM with maxval 255. The purpose of this is to allow meaningful pixel mixing.
Note that there is no equivalent exception when the input is PAM. If the PAM input tuple type is BLACKANDWHITE, the
PAM output tuple type is also BLACKANDWHITE, and you get no meaningful pixel mixing.
If you want PBM output with PBM input, use pamditherbw to convert pamscale's output to PBM. Also consider pbmreduce.
pamscale's function is essentially undefined for PAM input images that are not of tuple type RGB, GRAYSCALE,
BLACKANDWHITE, or the _ALPHA variations of those. (By standard Netpbm backward compatibility, this includes PBM,
PGM, and PPM images).
You might think it would have an obvious effect on other tuple types, but remember that the aforementioned tuple
types have gamma-adjusted sample values, and pamscale uses that fact in its calculations. And it treats a transâ
parency plane different from any other plane.
pamscale does not simply reject unrecognized tuple types because there's a possibility that just by coincidence you
can get useful function out of it with some other tuple type and the right combination of options (consider -linear
in particular).
Methods Of Scaling
There are numerous ways to scale an image. pamscale implements a bunch of them; you select among them with invocaâ
tion options.
Pixel Mixing
Pamscale's default method is pixel mixing. To understand this, imagine the source image as composed of square tiles.
Each tile is a pixel and has uniform color. The tiles are all the same size. Now take a transparent sheet the size
of the target image, marked with a square grid of tiles the same size. Stretch or compress the source image to the
size of the sheet and lay the sheet over the source.
Each cell in the overlay grid stands for a pixel of the target image. For example, if you are scaling a 100x200 imâ
age up by 1.5, the source image is 100 x 200 tiles, and the transparent sheet is marked off in 150 x 300 cells.
Each cell covers parts of multiple tiles. To make the target image, just color in each cell with the color which is
the average of the colors the cell covers -- weighted by the amount of that color it covers. A cell in our example
might cover 4/9 of a blue tile, 2/9 of a red tile, 2/9 of a green tile, and 1/9 of a white tile. So the target pixel
would be somewhat unsaturated blue.
When you are scaling up or down by an integer, the results are simple. When scaling up, pixels get duplicated. When
scaling down, pixels get thrown away. In either case, the colors in the target image are a subset of those in the
source image.
When the scale factor is weirder than that, the target image can have colors that didn't exist in the original. For
example, a red pixel next to a white pixel in the source might become a red pixel, a pink pixel, and a white pixel in
the target.
This method tends to replicate what the human eye does as it moves closer to or further away from an image. It also
tends to replicate what the human eye sees, when far enough away to make the pixelization disappear, if an image is
not made of pixels and simply stretches or shrinks.
Discrete Sampling
Discrete sampling is basically the same thing as pixel mixing except that, in the model described above, instead of
averaging the colors of the tiles the cell covers, you pick the one color that covers the most area.
The result you see is that when you enlarge an image, pixels get duplicated and when you reduce an image, some pixels
get discarded.
The advantage of this is that you end up with an image made from the same color palette as the original. Sometimes
that's important.
The disadvantage is that it distorts the picture. If you scale up by 1.5 horizontally, for example, the even numâ
bered input pixels are doubled in the output and the odd numbered ones are copied singly. If you have a bunch of one
pixel wide lines in the source, you may find that some of them stretch to 2 pixels, others remain 1 pixel when you
enlarge. When you reduce, you may find that some of the lines disappear completely.
You select discrete sampling with pamscale's -nomix option.
Actually, -nomix doesn't do exactly what I described above. It does the scaling in two passes - first horizontal,
then vertical. This can produce slightly different results.
There is one common case in which one often finds it burdensome to have pamscale make up colors that weren't there
originally: Where one is working with an image format such as GIF that has a limited number of possible colors per
image. If you take a GIF with 256 colors, convert it to PPM, scale by .625, and convert back to GIF, you will probaâ
bly find that the reduced image has way more than 256 colors, and therefore cannot be converted to GIF. One way to
solve this problem is to do the reduction with discrete sampling instead of pixel mixing. Probably a better way is
to do the pixel mixing, but then color quantize the result with pnmquant before converting to GIF.
When the scale factor is an integer (which means you're scaling up), discrete sampling and pixel mixing are identical
-- output pixels are always just N copies of the input pixels. In this case, though, consider using pamstretch inâ
stead of pamscale to get the added pixels interpolated instead of just copied and thereby get a smoother enlargement.
pamscale's discrete sampling is faster than pixel mixing, but pamenlarge is faster still. pamenlarge works only on
integer enlargements.
discrete sampling (-nomix) was new in Netpbm 9.24 (January 2002).
Resampling
Resampling assumes that the source image is a discrete sampling of some original continuous image. That is, it asâ
sumes there is some non-pixelized original image and each pixel of the source image is simply the color of that image
at a particular point. Those points, naturally, are the intersections of a square grid.
The idea of resampling is just to compute that original image, then sample it at a different frequency (a grid of a
different scale).
The problem, of course, is that sampling necessarily throws away the information you need to rebuild the original imâ
age. So we have to make a bunch of assumptions about the makeup of the original image.
You tell pamscale to use the resampling method by specifying the -filter option. The value of this option is the
name of a function, from the set listed below.
To explain resampling, we are going to talk about a simple one dimensional scaling -- scaling a single row of
grayscale pixels horizontally. If you can understand that, you can easily understand how to do a whole image: Scale
each of the rows of the image, then scale each of the resulting columns. And scale each of the color component
planes separately.
As a first step in resampling, pamscale converts the source image, which is a set of discrete pixel values, into a
continuous step function. A step function is a function whose graph is a staircase-y thing.
Now, we convolve the step function with a proper scaling of the filter function that you identified with -filter. If
you don't know what the mathematical concept of convolution (convolving) is, you are officially lost. You cannot unâ
derstand this explanation. The result of this convolution is the imaginary original continuous image we've been
talking about.
Finally, we make target pixels by picking values from that function.
To understand what is going on, we use Fourier analysis:
The idea is that the only difference between our step function and the original continuous function (remember that we
constructed the step function from the source image, which is itself a sampling of the original continuous function)
is that the step function has a bunch of high frequency Fourier components added. If we could chop out all the
higher frequency components of the step function, and know that they're all higher than any frequency in the original
function, we'd have the original function back.
The resampling method assumes that the original function was sampled at a high enough frequency to form a perfect
sampling. A perfect sampling is one from which you can recover exactly the original continuous function. The
Nyquist theorem says that as long as your sample rate is at least twice the highest frequency in your original funcâ
tion, the sampling is perfect. So we assume that the image is a sampling of something whose highest frequency is
half the sample rate (pixel resolution) or less. Given that, our filtering does in fact recover the original continâ
uous image from the samples (pixels).
To chop out all the components above a certain frequency, we just multiply the Fourier transform of the step function
by a rectangle function.
We could find the Fourier transform of the step function, multiply it by a rectangle function, and then Fourier
transform the result back, but there's an easier way. Mathematicians tell us that multiplying in the frequency doâ
main is equivalent to convolving in the time domain. That means multiplying the Fourier transform of F by a rectanâ
gle function R is the same as convolving F with the Fourier transform of R. It's a lot better to take the Fourier
transform of R, and build it into pamscale than to have pamscale take the Fourier transform of the input image dynamâ
ically.
That leaves only one question: What is the Fourier transform of a rectangle function? Answer: sinc. Recall from
math that sinc is defined as sinc(x) = sin(PI*x)/PI*x.
Hence, when you specify -filter=sinc, you are effectively passing the step function of the source image through a low
pass frequency filter and recovering a good approximation of the original continuous image.
Refiltering
There's another twist: If you simply sample the reconstructed original continuous image at the new sample rate, and
that new sample rate isn't at least twice the highest frequency in the original continuous image, you won't get a
perfect sampling. In fact, you'll get something with ugly aliasing in it. Note that this can't be a problem when
you're scaling up (increasing the sample rate), because the fact that the old sample rate was above the Nyquist level
means so is the new one. But when scaling down, it's a problem. Obviously, you have to give up image quality when
scaling down, but aliasing is not the best way to do it. It's better just to remove high frequency components from
the original continuous image before sampling, and then get a perfect sampling of that.
Therefore, pamscale filters out frequencies above half the new sample rate before picking the new samples.
Approximations
Unfortunately, pamscale doesn't do the convolution precisely. Instead of evaluating the filter function at every
point, it samples it -- assumes that it doesn't change any more often than the step function does. pamscale could
actually do the true integration fairly easily. Since the filter functions are built into the program, the integrals
of them could be too. Maybe someday it will.
There is one more complication with the Fourier analysis. sinc has nonzero values on out to infinity and minus inâ
finity. That makes it hard to compute a convolution with it. So instead, there are filter functions that approxiâ
mate sinc but are nonzero only within a manageable range. To get those, you multiply the sinc function by a window
function, which you select with the -window option. The same holds for other filter functions that go on forever
like sinc. By default, for a filter that needs a window function, the window function is the Blackman function.
Hanning, Hamming, and Kaiser are alternatives.
Filter Functions Besides Sinc
The math described above works only with sinc as the filter function. pamscale offers many other filter functions,
though. Some of these approximate sinc and are faster to compute. For most of them, I have no idea of the mathematâ
ical explanation for them, but people do find they give pleasing results. They may not be based on resampling at
all, but just exploit the convolution that is coincidentally part of a resampling calculation.
For some filter functions, you can tell just by looking at the convolution how they vary the resampling process from
the perfect one based on sinc:
The impulse filter assumes that the original continuous image is in fact a step function -- the very one we computed
as the first step in the resampling. This is mathematically equivalent to the discrete sampling method.
The box (rectangle) filter assumes the original image is a piecewise linear function. Its graph just looks like
straight lines connecting the pixel values. This is mathematically equivalent to the pixel mixing method (but mixing
brightness, not light intensity, so like pamscale -linear) when scaling down, and interpolation (ala pamstretch) when
scaling up.
Gamma
pamscale assumes the underlying continuous function is a function of brightness (as opposed to light intensity), and
therefore does all this math using the gamma-adjusted numbers found in a PNM or PAM image. The -linear option is not
available with resampling (it causes pamscale to fail), because it wouldn't be useful enough to justify the implemenâ
tation effort.
Resampling (-filter) was new in Netpbm 10.20 (January 2004).
The filter and window functions
Here is a list of the function names you can specify for the -filter or -windowoption. For most of them, you're on
your own to figure out just what the function is and what kind of scaling it does. These are common functions from
mathematics. Note that some of these make sense only as filter functions and some make sense only as window funcâ
tions.
point The graph of this is a single point at X=0, Y=1.
box The graph of this is a rectangle sitting on the X axis and centered on the Y axis with height 1 and base 1.
triangle
The graph of this is an isosceles triangle sitting on the X axis and centered on the Y axis with height 1 and
base 2.
quadratic
cubic
catrom
mitchell
gauss
sinc
bessel
hanning
hamming
blackman
kaiser
normal
hermite
lanczos
Not documented
Linear vs Gamma-adjusted
The pixel mixing scaling method described above involves intensities of pixels (more precisely, it involves individâ
ual intensities of primary color components of pixels). But the PNM and PNM-equivalent PAM image formats represent
intensities with gamma-adjusted numbers that are not linearly proportional to intensity. So pamscale, by default,
performs a calculation on each sample read from its input and each sample written to its output to convert between
these gamma-adjusted numbers and internal intensity-proportional numbers.
Sometimes you are not working with true PNM or PAM images, but rather a variation in which the sample values are in
fact directly proportional to intensity. If so, use the -linear option to tell pamscale this. pamscale then will
skip the conversions.
The conversion takes time. In one experiment, it increased by a factor of 10 the time required to reduce an image.
And the difference between intensity-proportional values and gamma-adjusted values may be small enough that you would
barely see a difference in the result if you just pretended that the gamma-adjusted values were in fact intensity-
proportional. So just to save time, at the expense of some image quality, you can specify -linear even when you have
true PPM input and expect true PPM output.
For the first 13 years of Netpbm's life, until Netpbm 10.20 (January 2004), pamscale's predecessor pnmscale always
treated the PPM samples as intensity-proportional even though they were not, and drew few complaints. So using -linâ
ear as a lie is a reasonable thing to do if speed is important to you. But if speed is important, you also should
consider the -nomix option and pnmscalefixed.
Another technique to consider is to convert your PNM image to the linear variation with pnmgamma, run pamscale on it
and other transformations that like linear PNM, and then convert it back to true PNM with pnmgamma -ungamma. pnâ
mgamma is often faster than pamscale in doing the conversion.
With -nomix, -linear has no effect. That's because pamscale does not concern itself with the meaning of the sample
values in this method; pamscale just copies numbers from its input to its output.
Precision
pamscale uses floating point arithmetic internally. There is a speed cost associated with this. For some images,
you can get the acceptable results (in fact, sometimes identical results) faster with pnmscalefixed, which uses fixed
point arithmetic. pnmscalefixed may, however, distort your image a little. See the pnmscalefixed user manual for a
complete discussion of the difference.
OPTIONS
In addition to the options common to all programs based on libnetpbm (most notably -quiet, see Common Options ),
pamscale recognizes the following command line options:
-width
-height
-xsize
-ysize
-xscale
-yscale
-xyfit
-xyfill
-reduce
-pixels
-xysize
These options determine the horizontal and vertical scale factors.
See The Scale Factors .
-reportonly
This causes pamscale not to scale the image, but instead to
report to Standard Output what scaling the options and the input image
dimensions indicate.
See -reportonly .
-nomix
This option selects discrete sampling as the
method of scaling .
-filter=functionName
This option selects resampling as the
method of scaling .
-window=functionName
This option selects a window function to modify the filter function
specified with -filter.
See Resampling .
-verbose
This option causes pamscale to issue messages to Standard Error about
the scaling.
SEE ALSO
pnmscalefixed(1), pamstretch(1), pamstretch-gen(1), pamditherbw(1), pbmreduce(1), pbmpscale(1), pamenlarge(1), pnâ
msmooth(1), pamcut(1), pnmgamma(1), pnmscale(1), pnm(1), pam(1)
HISTORY
pamscale was new in Netpbm 10.20 (January 2004). It was adapted from, and obsoleted, pnmscale. pamscale's primary
difference from pnmscale is that it handles the PAM format and uses the "pam" facilities of the Netpbm programming
library. But it also added the resampling class of scaling method. Furthermore, it properly does its pixel mixing
arithmetic (by default) using intensity-proportional values instead of the gamma-adjusted values the pnmscale uses.
To get the old pnmscale arithmetic, you can specify the -linear option.
The intensity proportional stuff came out of suggestions by Adam M Costello in January 2004.
The resampling algorithms are mostly taken from code contributed by Michael Reinelt in December 2003.
The version of pnmscale from which pamscale was derived, itself evolved out of the original Pbmplus version of pnmâ
scale by Jef Poskanzer (1989, 1991). But none of that original code remains.
DOCUMENT SOURCE
This manual page was generated by the Netpbm tool 'makeman' from HTML source. The master documentation is at
http://netpbm.sourceforge.net/doc/pamscale.html
netpbm documentation 29 June 2020 Pamscale User Manual(1)