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Field
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Description
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Auto
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Automatically decide a good method. This automatically chooses
an appropriate interpolation algorithm given the type of image and
the type of scaling. If you are aiming to maximize quality this is
the setting you should use. |
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Cubic
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Cubic interpolation. |
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Lanczos
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Interpolation using 3-lobed Lanczos window function. |
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Linear
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Linear interpolation. The Linear, Cubic and Lanczos methods are
progressively higher quality methods based around a weighted
average of pixels from the source image. However they are also
progressively slower. |
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NearestNeighbor
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Nearest neighbor interpolation. This is the fastest but also
the lowest quality method. It simply finds the nearest pixel in the
source image and maps it through to the destination image. However
it is also the only method which can be used for Indexed color
images. |
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Super
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Super sampling interpolation (cannot be used for enlarging).
The Linear, Cubic and Lanczos methods use weighted averages from a
limited number of pixels in the source image. For large size
reductions this may result in information from some pixels in the
source image being completely discarded. The super method aims to
work around this issue by increasing coverage to all the pixels in
the source image. It only works if an image is being reduced in
both height and width. |
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