ImageMagick v6 Examples --
Resize or Scaling (General Techniques)

Index

ImageMagick Examples Preface and Index

Resizing Images Ignore Aspect Ratio ('!') Only Shrink Larger ('>') Only Enlarge Smaller ('<') Fill Given Area ('^') Percentage Resize ('%') Pixel Count Limit ('@') During Image Reading Other Specialised Resize Operators Geometry - Resize just the last image Thumbnail - Resize with profile stripping Resample - Changing an images resolution Scale - Minify with pixel averaging Sample - Resize by row/column replication/deleting Magnify - double images size by "pixel scaling" Adaptive Resize - Small resizes without blurring Interpolative Resize - resize using interpolation method Liquid Rescale - Resize using Seam Carving Distort Resizing - free-form resizing Distort vs Resize - orthogonal vs cylindrical   Resizing Techniques Resizing with Colorspace Correction * * * Resizing with Gamma Correction Resizing in LAB colorspace Resizing in LUV colorspace Summary of Resizing using different colorspaces Resizing using a Sigmoidal Colorspace Sharpen Resized Images -- Photoshop Resize Resizing to Fill a Given Space -- OLD Resizing Line Drawings   Resize/Resampling Filters (separate section) Resampling by Nicolas Robidoux (separate section)

We look at enlarging and reducing images in various ways. The image remains intact and whole, but individual points of color merged or expanded to use up a smaller/larger canvas area.

Note that while this is related to the resolution of an image (number of pixels per real world length), that is more a product of how the image is eventually used, and not a true concern of Direct Image Processing.

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Resizing Images

The most obvious and common way to change the size of an image is to resize or scale an image. The content of the image is then enlarged or more commonly shrunk to fit the desired size. But while the actual image pixels and colors are modified, the content represented by the image is essentially left unchanged.

However resizing images can be a tricky matter. It can modify images in very detremental ways, and there is no 'best way' as what is best is subjective as to what you actually want out of the resize process.

Because there is no 'best' or 'perfect' way, there are a lot of options that you may like to consider. IM has always tried to provide options to give you the maximum scope of control in image resizing. There are hundreds of posiblilties, styles, and techniques, and even resize experts are consantally trying to find new and different ways of changing an images size.

Of course for most people, the normal default options is good enough as they were designed with general use in mind.

The resize operator has been very carefully designed to try to produce a very good result for real world images. That is not to say you can't use it for diagrams, or line drawings, though for that type of image you may need to use some of the more advanced options we'll look at later.

The foremost thing you should consider when specifying an image to resize is...
Do you really want to modify the image?

Resizing will cause drastic changes to an image, and avoiding or minimizing unwanted 'artifacts' is of greatest importance. Perhaps just a slight Shave of the edges, or a more general Crop of the image will produce a better and more desirable outcome than a wholesale resize of the image. It generally will look better and the area left will be a perfect copy of the original.

Because it is often better not to resize images...

The exception to this (there is always an exception) is if you actually specify a Resampling Filter using a "-filter" setting. In that case the normal "do nothing if image isn't resized" is overridden and the filter will be applied. However, many filters (even the default filters) can blur images slightly. It is part of their nature. So normally this 'short-circuit' for a no-op resize a good thing.

The argument to the resize operator is the area into which the image should be fitted. This area is not the final size of the image but the maximum size of the area into which the image is to be fitted. That is because IM tries to preserve the aspect ratio of the image more than the final (unless a '!' flag is given), but at least one (if not both) of the final dimensions should match the argument given image.

So let me be clear...

Aspect ratio is preserved basically so that any circles in the input image will remain a circle in the output image. That is the image will not be squashed or squeezed, only resized, unless you tell it otherwise.

For example here I attempt to fit two source images, one larger image and one smaller image into a square box 64x64 pixels in size.

As you can see a 64x64 square image was NOT produced by "-resize". In fact the images were only enlarged or reduced just enough so as to best fit into the given size.

Ignore Aspect Ratio ('!' flag)
If you want you can force "-resize" to ignore the aspect ratio and distort the image so it always generates an image exactly the size specified. This is done by adding the character '!' to the size. Unfortunately this character is also sometimes used for special purposes by various UNIX command line shells. So you may have to escape the character somehow to preserve it.

Only Shrink Larger Images ('>' flag)
Another commonly used option is to restrict IM so that it will only shrink images to fit into the size given.   Never enlarge.   This is the '>' resize option. Think of it only applying the resize to images 'greater than' the size given (its a little counter intuitive).

This option is often very important for saving disk space for images, or in thumbnail generation, when enlarging images generally may not desirable as it tends to produce 'fuzzy' enlargements.

The Only Shrink Flag ('>' flag) is a special character in both UNIX Shell and in Window batch scripts, and you will need to escape that character (using backslash '\>' in shell, and '^>' in windows batch). It is also special in and HTML web pages, so PHP scripts also may need some special handling.

Only Enlarge Smaller Images ('<' flag)
The inverse to the previous flag is '<', which will only enlarges images that are smaller than the given size, is rarely used.

The most notable use is with an argument such as '1x1<'. This resize argument will never actually resize any image. In other words it's a no-op, which will allow you to short circuit a resize operation in programs and scripts which always uses "-resize". Other than that you probably do not actually want to use this feature.

One such example of using this 'short circuit' argument is for the "-geometry" setting of "montage". See Montage and Geometry, caution needed for more details.

The Only Enlarge Flag ('<' flag) is a special character in both UNIX Shell and in Window batch scripts, and you will need to escape that character (using backslash '\<' in shell, and '^<' in windows batch). It is also special in and HTML web pages, so PHP scripts also may need some special handling.

Fill Area Flag ('^' flag)
As of IM v6.3.8-3 IM now has a new geometry option flag '^' which is used to resize the image based on the smallest fitting dimension. That is, the image is resized to completely fill (and even overflow) the pixel area given.

As it stands this option does not seem very useful, but when combined with either a centered (or uncentered) "-crop" or "-extent" to remove the excess parts of the image, you can fit the image so as to completely fill the area specified. Both the resize and the final image size arguments should be the same values.

Though the "-crop" is most logical, it may require an extra "+repage" to remove virtual canvas layering information. The "-extent" does not require this cleanup, but still allows the use of "-gravity" for positioning. See Cutting and Bordering for more information.

Also "-extent" can be used to pad out images that use the normal resize (with a "-background" color setting). See Thumbnails, Fit to a Given Space Summary, for more on this type of operation.

Remember this requires IM v6.3.8-3 or greater to make use of it. Otherwise use the older Resizing to Fill a Given Space technique below.

The Fill Area Flag ('^' flag) is a special character in Window batch scripts and you will need to escape that character by doubling it. For example '^^', or it will not work. See Windows Batch Scripting for this and other windowing particularities.

Percentage Resize ('%' flag)
Adding a percent sign, '%', to the "-resize" argument causes resize to scale the image by the amount specified.

Be warned however that the final pixel size of the image will be rounded to the nearest integer. That is, you will not generate partial pixels along the edge of the image! As a result the actual scale may not exactly match the scaling factor you provide, and may even be slightly different for X and Y directions, but it will be very close. (See Resize using Distort below).

If you really want to resize image such that the final size looks like it has a partial pixel size differences, you can use the General Distortion Operator and specifically the Scale-Rotation-Translate (see Distort Resizing below).

The Percentage Resize Flag ('%' flag) is a special character in Window batch scripts and you will need to escape that character by doubling it. For example '%%', or it will not work. See Windows Batch Scripting for this and other windowing particularities.

All these 'flag' options '!', '<', '>', '^', '%', and '@' are just on/off switches for the "-resize" operator. Just the character's presence (or absence) in the resize argument is what matters, not their position. They can appear at the start or end of the argument, or before or after individual numbers (though not in the middle of a number). That is, '%50' has exactly the same effect as '50%' though the latter is preferred for readability. Also '50%x30' actually means '50%x30%' and NOT 50% width and 30 pixel high as you might think. This is the case for all IM arguments using a 'geometry' style ('WxH' or '+X+Y') of argument. However offsets such as '+X+Y' are never treated as a percentage.

Resize using a Pixel Area Count Limit ('@' flag)
There is one final "-resize" option flag. The "at" symbol '@', will resize an image to contain no more than the given number of pixels. This can be used for example to make a collection of images of all different sizes roughly the same size. For example here we resize both our images to a rough 64x64 size, or 4096 pixels in size.

Note that the final image size is not limited to 64 pixels in height or width, but will have an area that is as close to (but smaller than) this size as IM can manage. That means one dimension will generally be slightly larger than 64 pixels and one will be slightly smaller.

In some ways this is an ideal compromise for thumbnailing images. See Area Fit Thumbnail Size.

You can also add the '>' flag to only shrink images that have more than the calculated number of pixels, while leaving images that are already smaller than that size.

Unfortunatally the '<', enlarge smaller images, flag is currently ignored when using 'Area Resize'.

Resize During Image Read
The resize operator can also be applied to images immediately after being read, before it is added to the current image sequence and the next image is read. That way a minimal amount of memory is needed to read in a lot of images. See Image Read Modifiers for more details.

For example...

The only problem with this technique is that no special resize options can be used, during the image read process.

Resize and transparency posed a problem for ImageMagick before v6.2.4, producing a black halo effect around light colored objects on transparency. This was researched and finally fixed from that version onward. For more detail of this old bug see Resize Halo Bug

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Other Resize Operators

Geometry - Resize just the last image

Geometry is a very special option. The operator behaves slightly differently in every IM command, and often in special and magical ways. The reasons for this is mostly due to legacy use and should be avoided if at all possible.

First, in "display" it is used to size and position the window of the image being displayed. This was its original usage and meaning when IM was first started. It was from this that its other 'resize' capabilities came about.

For "montage" "-geometry" is a setting that is saved until all the arguments have been read in. At this point it then defines the final tile (cell) size (or leaves it up to "montage" to work out) while the position arguments are used to specify the space surrounding the tile cells. See Montage Control Settings.

In "composite", "-geometry" is also saved until the end of arguments have been reached. Then it is used to resize and position the overlay image (the first image given) before it is overlaid onto the background image (the second image). For example see Composite Multiple Images.

As you can see it is used as a 'setting' in most IM commands, but in "convert" "-geometry" is both a special image resizing operator and a positioning setting.

What it does is to "-resize" just the last image in the current image sequence. This is the only image processing operator that is designed specifically to effect just the one image (the last one), in the current image sequence.

To complicate this special option further, the positional parts of the "-geometry" option is saved by "convert" command, just as it is in "composite". That is, any position is preserved for later use by the "-composite", to position the 'overlay' image, (the second last image in the current image sequence) over the 'background' image (the first image in the image sequence).

For this reason, you should limit the use of "-geometry" in "convert" commands to just before a "-composite" or "-layers composite" operations.

To summarize, this operator is only really useful after reading or creating a second image, just before you perform some type of Alpha Composition to process with those images.

For practical examples of using "-geometry" to resize/position images see Compositing Multiple Images.

Thumbnail - Resize with profile stripping

The "-thumbnail" operator is a variation of "-resize" designed specifically for shrinking very very large images to small thumbnails.

First it uses "-strip" to remove all profile and other fluff from the image. It then uses "-sample" to shrink the image down to 5 times the final height. Finally it does a normal "-resize" to reduce the image to its final size.

All this is to basically speed up thumbnail generation from very large files.

However for thumbnails of JPEG images, you can limit the size of the image read in from disk using the special option "-define jpeg:size={size}" setting. See Reading JPEG Images for more details. As such this speed improvment is rarely needed for JPEG in thumbnail generation, though profile stripping is still very important.

For other image formats, such as TIFF, both profile stripping and speed improvement is still vitally important. As such it is still the recommended way to resize images for thumbnail creation.

Before IM v6.5.4-7 the "-thumbnail" would strip ALL profiles from the image, including the ICC color profiles. From this version onward the color profiles will be preserved. If the color profile is not wanted then "-strip" all profiles.

Resample - Changing an image's resolution

Just as in the previous alternative resize operators, "-resample" is also a simple wrapper around the normal "-resize" operator.

Its purpose however is to adjust the number of pixels in an image so that when displayed at the given Resolution or Density the image will still look the same size in real world terms. That is, the given image is enlarged or shrunk, in terms of the number of pixels, while the image size in real world units will remain the same.

It is meant to be used for images that were read in from, or will be written out to, a program or device of a particular resolution or density. This is especially important for adjusting an image to fit a specific hardware output device, whether it is a display, or printer, or a postscript or PDF image format of a specific resolution. Just remember the real world size of the image does not change, only its resolution and of course the number of pixels used to represent the image.

For example, suppose you had an image that you scanned at a 300dpi (dots per inch). The image was saved with this resolution (density) or when you read it into IM, you specified it as a 300dpi image (using "-density"). Now you decide to display it on a screen that has a resolution of 90dpi, so you do a "-resample 90". IM will now resize the image by 90/300 or to 30% of the images original size and set the images new density to 90dpi. The image is now smaller in terms of the number of pixels used, but if displayed on a 90dpi display will appear at the same physical size as the original image you scanned. That is, it now has a resolution appropriate for a 90dpi display, so it will be displayed to the user at its original real world size.

A "-units" setting (with arguments 'PixelsPerInch' or 'PixelsPerCentimeter') may be required in some situations to get this operator to work correctly. This setting can also be important for output to Postscript and PDF image file formats.

Note that only a small number of image file formats (such as JPEG, PNG, and TIFF) are capable of storing the image resolution or density with the image data.

For formats which do not support an image resolution, or which are multi-resolution (vector based) image formats, the original resolution of the image must be specified via the "-density" attribute (see Density Image Meta-data) before being read in. If no density attribute has been set IM will assume it has a default density of 72dpi. Setting the density AFTER reading such an image will only affect its output resolution, and not affect its final size in terms of pixels.

Scale - Minify with pixel averaging

The "-scale" resize operator is a simplified, faster form of the resize command.

When enlarging an image, the pixels in the image are replicated to form a large rectangular blocks of color. Which is great for showing a clean unblurred magnification of an image.

For example here is a magnified view of one of the built-in tile patterns...

convert -size 8x8 pattern:CrossHatch30 -scale 800% scale_crosshatch.gif

Generally a single percentage value that is a multiple of 100% is used for the image enlargement so as to ensure all pixels are enlarged by the same amount, otherwise you can have different size pixel rows and columns producing large scale Moiré pattern.

For example here I badly scaled a smooth looking '50% gray checks' pattern, using a size that was not a multiple of the original images size. convert pattern:gray50 scale_gray_norm.gif convert pattern:gray50 -scale 36 scale_gray_mag.gif

When shrinking images, neighbouring pixels are averaged together to produce a new colored pixel. For example scaling an image to 50% of its original size will effectively average together blocks of 4 pixels to create a new pixel (assuming the image size is a multiple of 2 as well).

Caution is advised however as a scale reduced image can also generate Moiré patterns, unless the new image is an exact integer reduction (a technique known as 'binning'), which also requires the original image size to be some exact integer multiple of the final size.

Also a real-world photograph that has been heavily minified using "-scale" tends to look overlay sharp, with aliasing ('staircase') effects along sharp edges.

The pixel averaging of "-scale" allows it to generate 'pixelated' images. You basically reduce the size of the image to average the pixels, then enlarge again back to the image's original size.

You can use a mask to combine the above pixelated image with the original image, so as to 'hide' a much smaller 'naughty' bit present in the original image. See the Protect Someones Anonymity example for a demonstration of using this technique.

The algorithm is also designed to loop over rows of pixels then columns, which is inverted to that of "-resize". This may allow "-scale" to handle a "mpc:" disk cached image better.

Up until IM v6.4.7 the "-scale" still contained the old Resize Halo Bug.

Scale Internals (pixel mixing)...

In many ways the Scale Operator is similar to the normal Resize Operator, but using a 'Box' Resampling Filter. However it actually uses a completely different algorithm that is slightly more accurate in its results than that produced by a Box Filter.

The way the Box Filter works is to simply average any pixel (sample) that falls within the filters 'support window' (See Filter Support Expert Control. This means that when shrinking an image by a very small amount, a Box filtered Resize will only produce either exact pixel values, or an perfectly averaged pixel value.

The Scale Operator however uses a different algorithm that is known (for want of a better name) Pixel Mixing. Rather than generate the colors based on a 'average of pixels', within the 'support window' it uses a more accurite 'area of pixels' within the support window.

For example here I take 'checker board' pixel pattern, and shrink it by 2 pixels, comparing result of Scale against resizing using very simple Box & Triangle Filters.

The above shows the result you get for 'purely averaged', vs, 'pixel mixed', vs, 'linear interpolation'.

It also shows how Scale Operator is actually similar to that of a Triangle Filter, but only when doing very small downsizing of images. In other cases (strong downsize, upsizing, or exact interger sizing) it will produce results more like that of a Box Filter. Basically it generates something that is more like a mix between Box and Triangle filters depending on exactly how much the image is reduced in size.

You see similar effects when enlarging.

When enlarging a Box Filter will never produce 'averaged pixels', only pixel row/column duplications.

Scale however will generate averaged color pixels along the edges, again much like but not exactly the same as a Triangle Filter. Of course this effect is only really visible for small, non-integer enlargements, and in larger scalings, only along edges where in a more typical case you may get one or two averaged pixels.

In summary: Scale is much much faster than the normal Resize Operator, as it is less general in its image processing requirments. But it is also a completely different algorithm, producing a slightly different result when used to resize images using non-integer scales.

For more detail see the Pixel Mixing page, as well as the IM Forums Discussion, Upscaling a few pixels linearly. Special thanks goes to forum user atnbueno, who pointed out the above difference.

Sample - Resize by row/column replication/deleting

The "-sample" resize operator is the fastest resize operator, especially in large scale image reduction. In fact it is also even faster than the "-scale" operator (see above).

When enlarging or magnifying an image it only performs does pixel replication (as per the Box Filter, generating rectangular 'blocks' of pixel colors. However when shrinking an image "-sample" simply deletes rows and columns of pixels.

Because whole rows and columns of pixels are simply added or removed, "-sample" will generate no new or additional colors. This fact can be important for some image processing techniques such as resizing GIF animations.

Another way of looking at it is that the image has single individual pixels 'sampled' in a very even regular pattern across the whole image. You can think of the image as being divided into an array of regions, and one pixel from each region is selected for the resulting image.

However this 'sampling' of individual pixels (or wholesale row/column removal), can have rather horrible results, especially for images containing thin lines (in terms of width in pixels).

For example, here I draw a line but then reduce the image size resulting in only a line of dots.

This is a typical effect your get with image sampling, and is known as a sever Aliasing effect.

Offset of the Sampled Pixel

As of IM v6.8.4-7, the exact pixel being sampled in each sampling sub-region, is now defined as being the pixel at the mid-point of each region (or top-left center pixel if the sub-region is an even number of pixels). That means if you make a single pixel sample of an image, you will get the middle pixel of the image.

Before IM v6.8.4-7 the pixel selected was the top-left pixel of each region. However there are reports of some version where this may have been the bottom-right, or it may have even been slightly variable due to bugs.

This type of information is especially useful for images that being reduced by an integer division of the original image size. Such as when you are creating or sampling a pixelated image, or De-interlacing a Video Frames.

Also as of that version you can control exactly which pixel in each sub-region is selected using the define "sample:offset, which takes one or two percentage value(s) ('50' by default for mid-point).

Note a percentage is used as the in the general case the 'sampling sub-regions' may not align with pixel bounds. That is why it is a percentage is needed and not a 'pixel offset'. However if the image size is nicely divisible by the number of samples then you can easily calculate exactly which pixel you want from each sub-region.

For example if an image sampled so that you have 5 pixel sub regions, then (For example, sampling an image that has 100 pixel across, down to 20 pixel samples), then you can use an sampling offset percentage in the range 0 to 19.9 to select the first pixel in each region, 20.1 to 39.9 for second, and so on. In other words you can use a percentage value of either 10,30,50,70 and 90 to exactly specify exactly which pixel you want from each constant sized sampling region.

For more on sampling offsets see the IM Forum discussion Sample Points.

Magnify - Pixel Scaling

The "-magnify" option doubles the size of an image, but it does so by using a technique known as "Pixel Scaling" using the Scale2X algorithm. This algorithm tries to smooth the corners of pixels being enlarged, without adding extra colors. As such very small pixelated images enlarge more cleanly, while retaining the original colors and a "retro pixel look" of the smaller image.

Note the Virtual Pixel setting was used to ensure that magnify understood that this specific image 'wraps around' the image edges.

Before IM v6.8.4-10 magnify was just a wrapper around resize to double the size of the image. Not very useful and rarely used. Using "Pixel Scaling" makes this option a lot more useful. See IM Users forum on Pixel Scaling for more information.

A "Minify()" function is also often available in API's that halve the size of images but is just a wrapper around resize. However "-minify" is not available from the command line API, at least not at the time of writing.

Adaptive Resize - Small resizes without blurring

The "-adaptive-resize" operator uses the special Mesh Interpolation method to resize images.

For example here I resize a simple line, using first a normal "-resize", then again using "-adaptive-resize". If you look at a magnification of the two results... You can see the Adaptive Resized image on the right is a lot cleaner looking and less blurry than the image produced on the left using the normal "-resize" operator.

Basically the operator avoids the excessive blurring that a "-resize" operator can produce with sharp color changes. This works well for slight image size adjustments, particularly for magnification, and especially with images with sharp color changes.

However as with all pixel interpolative methods, it will produce aliasing, and Moiré effects when images are enlarged or reduced by more than 50%.

You can also generate the exact equivalent result using a Distort Resize operation but with the options "-filter point -interpolate mesh".  That is, resizing the image using a simple Mesh Interpolation lookup method, rather than a more complex resampling filter.

Interpolative Resize - Resize using an Interpolation Method

The "-interpolative-resize" operator is practically identical to the previous Adaptive Resize operator. However this operator will use the current "-interpolate" setting rather than a fixed 'Mesh' interpolation method.

If you use a "-interpolate" setting of 'Nearest', you will essentually get the equivelent of a Sample Operator. Similarly many of the other Simple Interpolation Methods, will equate to using the equivelent Interpolated Resize Filters. But there are a number of Interpolation methods such as Mesh that have no equivelence as a resize filter.

This is also an unscaled resize, which means it works great for enlargements, and small scale shrinking, but when shrinking by more that 50% sever Aliasing Effects can appear, as shown in the other 'sampling resize operators' above.

Liquid Rescale - Seam Carving

Just as Sampling an image resizes by directly removing or duplicating whole columns and rows from an image, the special IM operator "-liquid-rescale" also removes or duplicates columns and rows of pixels from an image to reduce/enlarge an image. The difference is that it tries to do so in a more intelligent manner.

Firstly, instead of removing a simple line of pixels, it removes a 'seam' of pixels. That is, the column (or row) that could zig-zag through the image, at angles up to 45 degrees.

Secondly it tries to remove seams that have the 'least importance' in terms of the image's contents. How it selects this is in terms of the image's energy, or more simply, the amount of color changes a particular 'seam' involves. The 'seam' with the least amount of changes will be removed first, followed by higher 'energy' seams, until the image is the size desired.

For more detailed information of liquid resizing and seam carving see, Wikipedia: Seam Carving, the YouTube Video Demo, and the PDF paper: Seam Carving for Content-Aware Image Resizing.

Here for example is the IM logo as it is resized smaller using the IM "-liquid-rescale" operator.

Notice how "-liquid-rescale" preserved the complex wizard, while squeezing up the less complex stars and title part of the image. It also squeeze the right foot of the wizard slightly, producing a little jaggedness in the edge of the cloak, just as it did to the wizard's thin but simple wand.

On the other hand the Sample Resize image, simply removed equally spaced columns of pixels, which resulted in the whole image becoming equally distorted. The stars are not preserved intact and all the edges have distinct but uniform aliasing effects.

Basically "-liquid-rescale" will produce a generally better looking 'squeezed' image without generating extra 'mixed colors' or blurring of the image. However you can get some slight but localized aliasing effects in one spot (the wizards wand in this case) rather than spreading that effect across the whole image.

It will also expand images, by 'doubling' up the seams found within the image.

As you can see tries to first double the amount of space between the various objects (where it can), spreading them out. Though in this case the left most star and the 'm' becomes distorted as the 'seams' going through these 'low energy' regions become grouped together.

Note however it will only double each seam once, and as such the technique starts to break down when images are expanded too much. A better method is often to resize the image larger first, then use liquid rescaling to reduce it to the desired size. Or to use "-liquid-rescale" in multiple smaller steps.

To show the effect of "-liquid-rescale" better here is an animation, as the same image is resized down to a very thin image, then enlarged again. This animation was created using the shell script animate_lqr.

Again notice how it tries to preserve the most complex parts of the image, as the image gets compressed into a smaller and smaller area. That is, the spaces in the title are preferentially compressed first, then the wizard's arm, then the right side of the wizard, leaving the most complex middle part of the wizard for the very end.

Especially look at how the stars get pushed together before they are finally effected by the resampling pixel removal that liquid rescaling implements. (See problems next)

You can think of liquid rescaling as trying to compress an image, like a sponge, with the open areas being compressed first leaving the bulky and more structured parts for last.

Seam Carving Problems

Liquid Resize, or Seam Carving, works purely by removing whole pixels from the image. As such, like sampling, it will not generate or merge colors together, and straight lines and patterns within the image may become heavily distorted by the operation. Basically it can result in serious Aliasing Effects, unless some method of smoothing is also applied.

Generally however the aliasing effects will be grouped and localised to the less complex areas of the image rather then spread thought the image. this is the only reason it works so well!

As a 'seam' can zig-zag through the image, the seams, can and often appear to go around complex objects, removing the space between the objects before attempting to compressing the objects themselves. Note for example how the word 'Image' in the above demonstration appears to get shoved under the other letters in the title without too much distortion. However this side to side movement limited to 45 degree angles.

For images with 'busy' backgrounds, and less 'busy' foreground objects such as photos containing peoples faces, the energy function can assume that the foreground object is less important that the background. This results in some serious detrimental side effects, that may require human intervention to resolve.

Liquid Rescaling, is currently a highly experimental operation added for IM v6.3.8-4. It requires the "liblqr" delegate library to be installed before it will work for you. At this time no expert user controls have been provided. Controls such as to modify the content energy function used, or use an user provided preservation/removal filter (adjusting that energy function), or access to the intermediate seam carved images, and functions that the library also provides. It is assumed that such controls will be provided in sometime in the future, as users demand them, and we get more internal control of the library functions. WARNING Do not expect this to remain, exactly as it is currently implemented. It is highly experimental, and is expected to change and expand in functionality.

Distort Resize - free-form resizing

All the above resize methods all have one limitation which we touched on earlier, they will round the size of the new image to an integer number of pixels, then map the old image's pixels to the new pixel array.

This has two effects. First when resizing to a very small size the X scale may not exactly match the Y scale of the resulting image (a slightly different aspect ratio). This difference is minor, and unless you get very small it is usually not noticeable.

The other effect is that you cannot resize an image to fit an area that contains a partial pixel edge, which can be important in further processing, such image overlays.

It also means you cannot use resize to just shift (translate) an image half a pixel to the right (without actual resize) even though the algorithm could quite easily do this.

With IM v6.3.6 the General Distortion Operator "-distort" will let you do this and more, using its Scale-Rotate-Translate distortion method. You can also do this using an Affine distortion based on movements of control points.

Note however that because the edge of the image can contain partial pixels, the final image will probably be 2 to 3 pixels larger than you probably would expect. The extra surrounding pixels will be mixed according to the current Virtual Pixel setting, which you typically set to be transparent.

For example here I resize the rose image to 90% (.9) of its original size, without rotation (0), shrinking it around the center of the image (the default control point if not specified)...

convert rose: -matte -virtual-pixel transparent \ +distort SRT '.9,0' +repage rose_distort_scale.png

It may not look like an improvement, in fact it has fuzzy edges, but it is an exact resize without adjustments for a final integer image size, just as you requested. Because of this the edges are fuzzy as the pixel colors are being spread over fractions of a pixel size, and not just to whole integers.

Note that I used the 'plus' form of "+distort" to allow this image's processing operator to set the final images size and offset on the Virtual Canvas correctly, for further processing and layering. If this offset is not desired it can be removed using "+repage" operator. But if left in place then the actual images location on the larger canvas will be preserved, allowing you to exactly position the image correctly with its 'fuzzy edges'.

Here I resized it so the top left corner (0,0) was moved .5 pixels to the right (to .5,0) and the rest of the image scaled around that control point...

convert rose: -matte -virtual-pixel transparent \ +distort SRT '0,0 .9 0 .5,0' +repage rose_distort_shift.png

Note that as the top edge did not actually move, it remained relatively sharp, while all the other edges became fuzzy. Here is a pixel magnification of the top corner, showing the transparency that was added by distort to provide sub-pixel resizing...

convert rose_distort_shift.png -crop 15x15+0+0 +repage \ -scale 600% rose_distort_shift_mag.png

You can see that the top edge remained sharp, while the left (and all other edges) are now semi-transparent.

And that is the point. You have exact control of the resize, and the final sub-pixel location of the resulting image. Not just a quantized fit of the resized image to an integer number of pixels. That is, the distort is an exact re-scaling and positioning of the image to fractions of a pixel, allow you to fit it precisely into other images.

This can become especially important when doing video work, where an imprecise resize of embeded images can produce 'jarring' effects.

Technically, image resizing is a simplified form of Image Distortion, both of which are techniques of image resampling. It's very fast 2-pass filtering technique, is limited to orthogonally aligned pixel scaling, and an integer number of pixels in the final result.

Affine, Transform

As of IM v6.4.2-8 the older "-affine" setting used with either "-transform" or "-draw" operators, provide a similar free-form resize capability. However in reality it is equivalent to a calling "+distort" with an 'AffineProjection' distortion method. As such all the previous Distort notes apply.

It does require more mathematics making it hard for the typical user to use. Generally you are better off using the above distortion method, which provides a number of alternative methods of specifying the affine distortion to be applied.

Distort vs Resize

If you actually want to do a direct comparison between using Distort vs Resize you will need to specifically limit the distortion of the image, so as to exactly match the resized image you care comparing it to. This is not a simple task.

To make this easier a special Resize Distortion Method was added to IM v6.6.9-2.

Here for example I greatly enlarge the built-in "rose:" using a fast Resize, and then using Distort...

If you look along the bottom edge of the rose, you will see that the Distort Operator actually produced a better cleaner result than the Resize Operator. With very little Blocking Artefacts that is common when enlarging images.

Other than this bottom edge, the rest of the image is practically identical, even when compared using a "flicker_cmp" script.

However remember that Distort is much slower than Resize, as it uses a more direct but much more complex Area Resampling technique, without the 2-pass speed optimizations that resize uses.

The real difference in the above two images is that the Distort Operator uses a two dimensional Elliptical Area Resampling filter method (also known as cylindrical filtering or resampling) for its image processing. This is slower than the one dimensional, two pass resampling method used by all the other resize methods shown in this section. It is also why it produced a better result along the diagonal bottom edge of the enlarged rose image above. It is not limited to just horizontal and vertical filtering. You can see the effects this has on ringing in the examples on Ringing Artefacts.

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Resizing Techniques

Resizing with Colorspace Correction

While resize works very well, most people do not use it correctly. Even I normally just use resize directly on images, as is, and thus technically I resize images incorrectly.

Images are typically stored using a non-linear "sRGB" colorspace, or with gamma correction. See Human Color Perception for details. But resize (like most other image processing operators) is a mathematically linear processor, that assumes that image values directly represent a linear color brightness.

The colorspace "sRGB" is basically contains a gamma correction of roughly 2.2. Actually it is more complex than that involving two separate curves. See wikipedia, sRGB and W3org, sRGB the Default Colorspace of the Internet.

As of version 6.7.5 ImageMagick follows this convention and defines the default colorspace of images (at least for most image file formats) to be sRGB. This means we simply need to use the "-colorspace" to transform the image to a linear space before doing the resize.

Using color correction on a low-quality Q8 version of IM (see Quality) is not recommended due to the loss of precision such a low memory quality provides.

The NASA image "Earth's City Lights" is a very extreme case where non-linear colorspace effects have a big impact on the results of resizing the image.

Here we directly resize the image without colorspace correction...

And here we convert from a non-linear sRGB, to linear RGB, then resize them, and convert it back again...

As you can see the 'lights' in the images are much much brighter as they are not so heavilly influenced by the non-linear colorspace of the source image. Though most images do not have as great an impact as can be seen in the above, it is present, and can have many effects.

The main effect that can be seen from the non-linear effects of sRBG is that darker colors are saved as much darker values (so as to be more perceptually relevent. But as they are darker, they do not get processed correctly mathematically, so the resulting sRGB image is darker than an image processed in linear colorspace like RGB (or LAB or LUV).

Also see Color Processing Real Images and Drawing with Gamma and Colorspace Correction.

The same correct colorspace handling also applies to the use of distort (elliptical filter), image blurring, and can have great effects on image quantization, dithering and ordered dithering. This is looked at in detail in resampling filters.

WARNING: RGB colorspace may produce clipping problems along edges involving strong primary color changes (not just between black and white). See the next section.

		In versions of IM older than v6.7.5, in which the default input colorspace was 'RGB'. The 'sRGB' colorspace actually meant "converted from sRGB to linear-RGB". The result was the two labels were swapped! Wierd but true. Because of this older versions of ImageMagick would need to do the above colorspace correction with those colorspace names swapped. Like this... convert earth_lights_4800.tif -colorspace sRGB \ -resize 500 -colorspace RGB earth_lights_colorspace.png *** This example is depreciated *** Note that the "-colorspace RGB" operation was not actually needed, as it was automatically performed when saving to PNG image file format. The above was developed from an IM Forum Discussion Correct Resize.

Resize with Gamma Correction

This is how to correctly resize images using gamma correction only.

An alternative to the gamma inverse operation "-gamma 0.454545" is to use "-evaluate POW 2.2".

Note that gamma correction is only a rough match to properly converting images from/to sRGB colorspace, but it is so close that you would be hard pressed to see any difference between colorspace vs gamma correction.

Gamma correction also does not play around with the IMv6 RGB/sRGB colorspace settings, so may be a better choice when the exact version may be unknown.

You may also like to look at the "-auto-gamma" operator, which tries to adjust the image, do as to produce equal amounts of light and dark areas (assuming the image is in a linear colorspace).

Resizing in LAB colorspace

One problem with using sRGB, RGB or even XYZ colorspace for resizing or for any sort of image processing is that the 3 color channels not only represent color but also intensity or brightness. That means that if one channel becomes distorted (such as by being clipped) the pixel color will also become distorted and can result in a funny look.

The LAB colorspace is not only a linear colorspace, but was designed so that intensity (L channel) is separated from two color channels (A^* and B^* channels). That means that if any one of the channels becomes clipped, it does not generate color skew.

It also means that generally none of the channels are actually close to clip limits unless you specifically deal with pure black and white images, which is uncommon in real life images.

As such by processing images using LAB colorspace, actually works better, and avoids clipping and color skew, that you may get when using a RGB or XYZ colorspace.

Before IM v6.7.8-2 LAB values for A* and B* channels was stored using signed integers, stored in an unsigned integer memory space. This created a discontinuity between negative and positive values, that did not allow normal processing to work, only image format converstions. This meant that in older versions of IM, image processing in LAB colorspace did not work, especially when a color involving both positive to negative values were involved. That when working with colors that change between blue-yellow and red-green. After this release the values were stored internally using a 50% bias, which removed that discontinuity, and thus allowing linear operations to work as expected.

For resize involving 'sharpening' resampling filters (very commonly used), using Lab colorspace will also moderate extreme intensity changes, that can generate overly strong (and range clipped) Ringing Artefacts, in the primary RGB colors.

For example...

As you can see while the edge of the rose was clipped in linear RGB colorspace, it was not clipped in LAB colorspace.

In RGB colorspace the bottom edge of the rose sees a color change from near pure white to near pure red, causing a strong (negative) change in the 'green' and 'blue' channels. This produces a very strong 'negative lobe' ringing effect which is then clipped in RGB colorspace. The final result is a severe color distortion, due to the filters sharpening effect.

In LAB colorpsace, the shift from white to red is not nearly as strong in either intensity or in the color channels, and so while we get good sharpening in the intensity, neither it nor the color channels is clipped, thus avoiding color distortion.

The result is a far better resized image, with a more appropriate sharpening effect from the filter. Simply by separating intensity from color.

Resize using LUV colorspace

As of IM v6.7.8-8 IM also implements a closely related colorspace LUV. They both designed to be perceptually uniform (linear), and even share the same critical intensity 'L' or 'Lighness' channel results, though calculate the color channels differently.

The main difference is that LUV color axis were adjusted so as to have perceptually equal color deltas (color differneces), which results in a slightly different color scale to LAB colorspace, though intensity remains the same between the two. See Adams chromatic valence color space.

The results for resizes between LAB and LUV is practically identical.

See Colorspace for more information about these two colorspaces..

Summary of Resizing using different colorspaces
Or why not use LAB or LUV for resize?

Well because like sRGB, LAB and LUV colorspace is a non-linear perceptual colorspace! And the mathematics was only ment to be applied to linear values.

For example here is the results of resizing the "Earth's City Lights" image in 'Lab' colorspace.

The result is practically identical to what you get if you had resized directly in the perceptual sRGB colorspace.

But is resizing in a perceptual colorspace really a bad thing? Really that is a debatable point. It seems avoiding color channel clipping, while color slew (unequal changes to differnet color channels) is less important

But then a LAB and LUV images are linear, perceptually! As such perhaps blending colors (which is what the Resampling Filters actually do) in a linear-perceptual colorspace may actually be a good thing to do.

Just one final point, sRGB is only perceptually-linear in intensity along the primary color components. It is not actually perceptually-linear in color, so it remains a bad colorspace to doing any form of image resizing.

Nicolas Robidoux summed it up nicely with...

Generally, the linear light colour spaces (linear RGB and XYZ) produce exaggerated dark halos, and the "perceptual" colour spaces (sRGB, LAB, LUV) produce exaggerated light halos. If you think about it for a minute, this makes complete sense, because the perceptual colour spaces pack lots of bits at the darker end of the intensity spectrum, and "hollow out" the lighter end, so as to mimick the HVS (Human Visual System). So, 1 unit of dark overshoot gets you less "far" in sRGB than in linear RGB, but 1 unit of light overshoot gets you less "far" in linear RGB than sRGB. Sigmoidization (see next) treats dark and light overshoots equally, and generally dampens the extremes of both.

Resizing using a Sigmoidal Colorspace

In a lengthy discussion on the ImageMagick Discussion Forums, Sigmoidal minimization of resampling filter haloing. A new technique has been developed in which rather than trying to resize images in a linear color space, the image is resized in a modified colorspace using the Sigmoidal Color Modifier Operator (-sigmoidal-contrast). This can reduce the clipping of extreme halo or Ringing Artefacts that may develop along very sharp edges.

For example, here is a sequence of 'improving' resize techniques, that have been discussed on the Digital Image Processing Forums...

Essentially what the final example in the above does is to decrease the contrast of the image, compressing mid-tone greys into a tighter linear range, while bringing the extreme values further from the clipping edges, before resizing. It then removes that modification afterward. This in turn de-emphases the extreme effects of color values, while still allowing the filter to process the mid-tones, in a linear fashion, so as to reduce color distortions.

In many ways this is similar to resizing images in the default non-linear sRGB colorspace (which is a far too common practice), but works equally well for both bright and dark ringing artifacts. That is it is symmetrical over the whole range of color values, where as resizing in sRGB colorspace works only from the lower dark end of the color range (blue and green values in the above). That is it is a much more controlled technique.

It has also been commented that that this sigmoidal variation may only work well for enlargements. Also try different values for the sigmoidal contrast intensity (6.5 in the above) for different images.

Remember, just as with every resize technqiue, the results are highly subjective, and may not be good for all image types.

Sigmoidal transform essentually generates a special DIY non-linear colorspace, that builds on the previous results that was obtained when using a non-linear perceptual colorspace (sRGB). Note that resizing (distorting) an image with non-linear color channels in RGB colorspace, can lead to slightly different results in each color channel. This results in a slight color shift (as opposed to a color being clipped as we saw earilier). This is only a problem with non-linear colorspaces with mixed color-intensity channels, such as sRGB, or a sigmoidal colorspace.

Unsharped Resizing (USM) -- Photoshop Resize Technique

Often resizing images (either smaller or larger) adds some fuzziness (Blurring Artefacts to the image. Because of this many people like to play around with the various filters (see Resampling Filters), to try and make the results sharper. But this can add other Resize Artefacts to the image results.

One method that is commonly used is to sharpen the image after resizing. Typically this is done using the special and weirdly named, Unsharp Operation, which contains even more controls to control the quality of the results.

For example, lets 'unsharp' the results of the very blurry 'Spline' filtered image...

As you can see sharpening the image after the resize improves the results. Looking at the stars and hat details in particular. You get a very good sharp image without any aliasing, ringing or even dimming of effects.

While a Spline Filter is not particularly good filter to begin with, this method of sharpening (actually 'unsharping') will work for ANY filter. It also provides more controls to fine-tune the result.

In actual fact this is what 'photoshop' does to improve the quality of its resized images, though I do not know what settings it uses for the Unsharp Operation. A technique known as USM

The "GIMP" default (radius=6, amount=0.5, threshold=0) for unsharp is equivalent to "-unsharp 12x6+0.5+0", and this is correct (other than ignoring that GIMP sets a hard radius at twice sigma). However remember you really do not need to specify the kernel radius in ImageMagick, so a value of "-unsharp 0x6+0.5+0" will work better.

See also the IM forum topic unsharp parameters in GIMP.

The post Image Resizing suggests using "-unsharp 0x0.75+0.75+0.008" as being good for images larger than 500 pixels.

While an Open Photography Forum discussion Downsampling with ImageMagick suggests "-unsharp 1.5x1+0.7+0.02".

Resizing to Fill a Given Space

Basically: Resizing a large image to completely fill a specific image size but cropping any parts of the image that do not fit.

As of IM v6.3.8-3 a new resize flag '^' will let you do this directly as a single resize step. These examples represents an alternative method that can be used for users with older versions of IM. See Resize Fill Flag above.

The solution is rather tricky, as the normal user requirement when resizing images is to fit the whole of an image into a given size. As the aspect ratio of the image is preserved, that leaves extra, unused space in the area you are trying to fill.

Here we try to resize an image to fill a 80x80 box.

convert logo: -resize 80x80\> \ -size 80x80 xc:blue +swap -gravity center -composite \ space_resize.jpg

In the above we added a backdrop canvas to pad out the unused parts of the resize box to show the space we wanted the image to fill, but it wasn't filled, as it preserved the image's aspect ratio.

Now if all your images are either landscape style (they are wider than they are high) then you can of course just resize the image to fit either the height or width of the area, then use "-crop" to cut the image to fit it exactly.

convert logo: -resize x80 \ -gravity center -crop 80x80+0+0 +repage space_crop.jpg

The problem is that, the above will only handle landscape style images. It will fail badly if the image is portrait style (higher than it is wide).

This of course can be solved in a script by first getting the image's dimensions, and then picking the right method to fit the image into the space needed. But a better solution would be to have IM do all the work for all images. The solution within IM is to process the image by resizing each of the images dimension separately. Then picking the larger image of the two results.

To make this easier, resize itself has a built-in test option which will only resize an image if that would make the image larger. This allows use a very nifty solution to our problem.

convert logo: \ -resize x160 -resize '160x<' -resize 50% \ -gravity center -crop 80x80+0+0 +repage space_fill.jpg

In the above, the second resize in the series will only resize if the width produced by the first resize was smaller than the area we are trying to fill.

The specific order of the resizes (height first, then width) was chosen, as most images are photographs which are usually longer horizontally. With the above ordering, such a case will result in the second resize operation being skipped.

If your images are more often portrait images (longer vertically) then change the arguments to resize the image by height first, then width. For example...

convert logo: \ -resize 160x -resize 'x160<' -resize 50% \ -gravity center -crop 80x80+0+0 +repage space_fill_2.jpg

The result of both of these examples should be very similar, and the command will work for both landscape and portrait styles of image, though it works better for one sort.

The biggest problem with this method is that the image is now being resized 2 to 3 times, producing extra blurring and other possible artifacts in the final result. To reduce this, the initial resizes are performed at double the final dimensions, which assumes the original image is at least 3 or more times the size of the final desired result. Not a problem for thumbnail production, but something to keep in mind.

Resizing Line Drawings

Doing a strong resize of an image containing thin lines can represent a big problem...

Resizing images to very small thumbnails, causes thin lines that are only a few pixels wide to fade and disappear into the background. This can get so bad that I have seen thumbnails of a line drawing which looked completely blank! That is, every detail of the original drawing 'disappeared', making the thumbnail rather useless.

If this is problem there are a few techniques that can help...

• Resize then adjust contrast to make lines more visible. Though this will make the lines more aliased (staircased). It also has limits to just how far this technique can be used.

• Blur and threshold the image (a method very similar to morphological 'dialate' or 'erode') so as to produce make single pixel lines about 300% thicker. Now after resizing by 1/3 the image will be smaller but the lines will remain just as strong and visible as before.

• Thicken lines using Thicken Morphology techniques.
  You may like to do the resize in stages, thickening and resizing the image by 50% at a time until you get to the final size.
  Though as the spacing between the lines shrinks you may find you end up with more of a 'blob' than a line drawing. That is you may get the opposite problem. Adjusting the ratio of thickening to resize however should produce an acceptable result.

• Separate the line edging in an image from the areas of solid colors, and resize each by different methods (lines using the above). Afterward the two parts can be merged back together again, allowing you to preserve the line edging of the image.
  This will in effect reproduce the effect you often get when resizing vector images.

• Convert image to a vector image, then resize. This can be tricky but also can produce the best possible result for resizing line drawings, with perfect anti-aliased (sharp) edges, and clear images.

If you come up with some other way of effectively resizing line drawings, or tried some of the above technqiues, please let me (and other IM users) know about it.

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Created: 15 March 2004
Updated: 10 October 2012 (split resampling filters to separate section)
Updated: 10 October 2012
Author: Anthony Thyssen, <Anthony.Thyssen@gmail.com>
Examples Generated with:
URL: https://legacy.imagemagick.org/Usage/resize/