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. 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. 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.
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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. 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. 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. Here I resized it so the top left corner 0,0 was moved. Here is a pixel magnification of the top corner, showing the transparency that was added by distort to provide sub-pixel resizing 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. Technically, image resizing is a simplified form of Image Distortion , both of which are techniques of image resampling.
Affine, Transform As of IM v6. 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. Here for example I greatly enlarge the built-in "rose:" using a fast Resize , and then using Distort With very little Blocking Artefacts that is common when enlarging images. 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.
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. Actually it is more complex than that involving two separate curves. As of version 6. 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. Here we directly resize the image without colorspace correction Though most images do not have as great a 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.
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. See the next section.
In versions of IM older than v6. 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 Resize with Gamma Correction This is how to correctly resize images using gamma correction only. 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.
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. 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.
Before IM v6. 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. For example 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. 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. And the mathematics was only ment to be applied to linear values. 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 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. 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.
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 artefacts.
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.
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. 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.
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. It also provides more controls to fine-tune the result.
Yozshur A couple of comments: It has plenty of actions and options as imagemagick. It generally will look better and downsampel area left will be a perfect copy of the original. Pierz 5 6. Any suggestions on how to correct this without having to take the spaces out of my folder names? ImageMagick examples mostly using php Imagick examples. Tested on Ubuntu Post as a guest Name. What is the easiest tool to do so?
Meztizil The blur step helps with aliasing issues; using a good Software for the rest of us? My wife often use this code to resize images for a livejournal. However, a particular file has gummed up the script. And if you want really fast JPEG image resizing — try epeg as mentioned here and there — which needs to be built from source. You can easily modify this command to perform other actions.
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As of IM v6. The following command asks ImageMagick to resize an image to pixels in width and pixels in height:. If you want to force the image to become imagrmagick specific size — even if it messes up the aspect ratio — add an exclamation point to the dimensions:. Best cameras and lenses ImageMagick is the package you want. Note that while this is related to the resolution of an image number of pixels per real world lengththat is more a product of how the image is eventually used, and not a true concern of Direct Image Processing.