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