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fix spelling
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@ -15,11 +15,11 @@ Bicubic and Bilinear downscaling
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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From very begining :py:attr:`~PIL.Image.BILINEAR` and
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:py:attr:`~PIL.Image.BICUBIC` filters was based on afine transformations
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:py:attr:`~PIL.Image.BICUBIC` filters was based on affine transformations
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and uses fixed number of pixels from source image for every destination pixel
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(that was 2x2 pixels for :py:attr:`~PIL.Image.BILINEAR` and 4x4 for
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:py:attr:`~PIL.Image.BICUBIC`). This gave an unsatisfied result for downscaling.
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At the same time high quality convolutions-based algorithm with flexeible kernel
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At the same time high quality convolutions-based algorithm with flexible kernel
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was used for :py:attr:`~PIL.Image.ANTIALIAS` filter).
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Starting from 2.7 high quality convolutions-based algorithm is used for all of
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@ -36,7 +36,7 @@ New :py:attr:`PIL.Image.LANCZOS` constant was added instead of
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:py:attr:`~PIL.Image.ANTIALIAS`.
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When :py:attr:`~PIL.Image.ANTIALIAS` was initially added, it was the only
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high-qality filter based on convolutions. It's name was supposed to reflect
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high-quality filter based on convolutions. It's name was supposed to reflect
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this. Starting from 2.7 all resize method are based on convolutions. All of them
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are antialias from now. And the real name of :py:attr:`~PIL.Image.ANTIALIAS`
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filter is Lanczos filter.
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@ -53,7 +53,7 @@ the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This was fixed.
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Bicubic upscaling quality
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^^^^^^^^^^^^^^^^^^^^^^^^^
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:py:attr:`~PIL.Image.BICUBIC` filter for affine trnsformations was producing
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:py:attr:`~PIL.Image.BICUBIC` filter for affine transformations produced
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sharp, slightly pixelated image for upscaling. Bicubic for convolutions is
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more soft.
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@ -61,7 +61,7 @@ Resize performance
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^^^^^^^^^^^^^^^^^^
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In most cases convolution is more expensive algorithm for downscaling because
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it tekes in account all pixels of source image. Therefore
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it takes in account all pixels of source image. Therefore
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:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters
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performance can be lower than before. On the other hand quality of
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:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` was close to
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@ -84,7 +84,7 @@ Antialias was chosen because all other filters gave poor quality for reduction.
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Starting from Pillow 2.7 :py:attr:`~PIL.Image.ANTIALIAS` replaced with
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:py:attr:`~PIL.Image.BICUBIC`, because it faster and
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:py:attr:`~PIL.Image.ANTIALIAS` doesn't give any advantages after
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downscaling with libJPEG, which uses supersampling internaly, not convolutions.
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downscaling with libJPEG, which uses supersampling internally, not convolutions.
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Image transposing
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-----------------
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@ -108,7 +108,7 @@ sequential applying of series of box filters. New implementation is based on
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"Theoretical foundations of Gaussian convolution by extended box filtering" from
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Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
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implementations uses Gaussian blur internally, all changes from this chapter
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alse applyable to it.
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also applicable to it.
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Blur radius
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^^^^^^^^^^^
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@ -119,7 +119,7 @@ For example for blurring image with actual radius 5 you were forced
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to use value 10. This was fixed. For now the meaning of the radius
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is the same as in other software.
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If you used a Gaussian blur with some radius value, you need to devide this
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If you used a Gaussian blur with some radius value, you need to divide this
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value by two.
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Blur Performance
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@ -138,7 +138,7 @@ Blur quality
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^^^^^^^^^^^^
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Previous implementation takes in account only source pixels within
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2 * standard deviation radius for every destination pixel. This was not enought,
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so qulity was worse compared to other Gaussian blur software.
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2 * standard deviation radius for every destination pixel. This was not enough,
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so quality was worse compared to other Gaussian blur software.
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The new implementation does not have this drawback.
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