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Pillow 2.7.0
============
Image resizing filters
----------------------
Image resizing methods :py:meth:`~PIL.Image.Image.resize` and
:py:meth:`~PIL.Image.Image.thumbnail` take a `resample` argument, which tells
which filter should be used for resampling. Possible values are:
:py:attr:`PIL.Image.NEAREST`, :py:attr:`PIL.Image.BILINEAR`,
:py:attr:`PIL.Image.BICUBIC` and :py:attr:`PIL.Image.ANTIALIAS`.
Almost all of them were changed in this version.
Bicubic and bilinear downscaling
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
From the beginning :py:attr:`~PIL.Image.BILINEAR` and
:py:attr:`~PIL.Image.BICUBIC` filters were based on affine transformations
and used a fixed number of pixels from the source image for every destination
pixel (2x2 pixels for :py:attr:`~PIL.Image.BILINEAR` and 4x4 for
:py:attr:`~PIL.Image.BICUBIC`). This gave an unsatisfactory result for
downscaling. At the same time, a high quality convolutions-based algorithm with
flexible kernel was used for :py:attr:`~PIL.Image.ANTIALIAS` filter.
Starting from Pillow 2.7.0, a high quality convolutions-based algorithm is used
for all of these three filters.
If you have previously used any tricks to maintain quality when downscaling with
:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters
(for example, reducing within several steps), they are unnecessary now.
Antialias renamed to Lanczos
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A new :py:attr:`PIL.Image.LANCZOS` constant was added instead of
:py:attr:`~PIL.Image.ANTIALIAS`.
When :py:attr:`~PIL.Image.ANTIALIAS` was initially added, it was the only
high-quality filter based on convolutions. It's name was supposed to reflect
this. Starting from Pillow 2.7.0 all resize method are based on convolutions.
All of them are antialias from now on. And the real name of the
:py:attr:`~PIL.Image.ANTIALIAS` filter is Lanczos filter.
The :py:attr:`~PIL.Image.ANTIALIAS` constant is left for backward compatibility
and is an alias for :py:attr:`~PIL.Image.LANCZOS`.
Lanczos upscaling quality
^^^^^^^^^^^^^^^^^^^^^^^^^
The image upscaling quality with :py:attr:`~PIL.Image.LANCZOS` filter was
almost the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This has been fixed.
Bicubic upscaling quality
^^^^^^^^^^^^^^^^^^^^^^^^^
The :py:attr:`~PIL.Image.BICUBIC` filter for affine transformations produced
sharp, slightly pixelated image for upscaling. Bicubic for convolutions is
more soft.
Resize performance
^^^^^^^^^^^^^^^^^^
In most cases, convolution is more a expensive algorithm for downscaling
because it takes into account all the pixels of source image. Therefore
:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters'
performance can be lower than before. On the other hand the quality of
:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` was close to
:py:attr:`~PIL.Image.NEAREST`. So if such quality is suitable for your tasks
you can switch to :py:attr:`~PIL.Image.NEAREST` filter for downscaling,
which will give a huge improvement in performance.
At the same time performance of convolution resampling for downscaling has been
improved by around a factor of two compared to the previous version.
The upscaling performance of the :py:attr:`~PIL.Image.LANCZOS` filter has
remained the same. For :py:attr:`~PIL.Image.BILINEAR` filter it has improved by
1.5 times and for :py:attr:`~PIL.Image.BICUBIC` by four times.
Default filter for thumbnails
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In Pillow 2.5 the default filter for :py:meth:`~PIL.Image.Image.thumbnail` was
changed from :py:attr:`~PIL.Image.NEAREST` to :py:attr:`~PIL.Image.ANTIALIAS`.
Antialias was chosen because all the other filters gave poor quality for
reduction. Starting from Pillow 2.7.0, :py:attr:`~PIL.Image.ANTIALIAS` has been
replaced with :py:attr:`~PIL.Image.BICUBIC`, because it's faster and
:py:attr:`~PIL.Image.ANTIALIAS` doesn't give any advantages after
downscaling with libjpeg, which uses supersampling internally, not convolutions.
Image transposition
-------------------
A new method :py:attr:`PIL.Image.TRANSPOSE` has been added for the
:py:meth:`~PIL.Image.Image.transpose` operation in addition to
:py:attr:`~PIL.Image.FLIP_LEFT_RIGHT`, :py:attr:`~PIL.Image.FLIP_TOP_BOTTOM`,
:py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_180`,
:py:attr:`~PIL.Image.ROTATE_270`. :py:attr:`~PIL.Image.TRANSPOSE` is an algebra
transpose, with an image reflected across its main diagonal.
The speed of :py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_270`
and :py:attr:`~PIL.Image.TRANSPOSE` has been significantly improved for large
images which don't fit in the processor cache.
Gaussian blur and unsharp mask
------------------------------
The :py:meth:`~PIL.ImageFilter.GaussianBlur` implementation has been replaced
with a sequential application of box filters. The new implementation is based on
"Theoretical foundations of Gaussian convolution by extended box filtering" from
the Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
implementations use Gaussian blur internally, all changes from this chapter
are also applicable to it.
Blur radius
^^^^^^^^^^^
There was an error in the previous version of Pillow, where blur radius (the
standard deviation of Gaussian) actually meant blur diameter. For example, to
blur an image with actual radius 5 you were forced to use value 10. This has
been fixed. Now the meaning of the radius is the same as in other software.
If you used a Gaussian blur with some radius value, you need to divide this
value by two.
Blur performance
^^^^^^^^^^^^^^^^
Box filter computation time is constant relative to the radius and depends
on source image size only. Because the new Gaussian blur implementation
is based on box filter, its computation time also doesn't depends on the blur
radius.
For example, previously, if the execution time for a given test image was 1
second for radius 1, 3.6 seconds for radius 10 and 17 seconds for 50, now blur
with any radius on same image is executed for 0.2 seconds.
Blur quality
^^^^^^^^^^^^
The previous implementation takes into account only source pixels within
2 * standard deviation radius for every destination pixel. This was not enough,
so the quality was worse compared to other Gaussian blur software.
The new implementation does not have this drawback.

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Pillow 2.7
==========
Image resizing filters
----------------------
Image resizing methods :py:meth:`~PIL.Image.Image.resize` and
:py:meth:`~PIL.Image.Image.thumbnail` takes `resample` argument, which tells
what filter should be used for resampling. Possible values are:
:py:attr:`PIL.Image.NEAREST`, :py:attr:`PIL.Image.BILINEAR`,
:py:attr:`PIL.Image.BICUBIC` and :py:attr:`PIL.Image.ANTIALIAS`.
Almost all of them was changed in this version.
Bicubic and Bilinear downscaling
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
From very begining :py:attr:`~PIL.Image.BILINEAR` and
:py:attr:`~PIL.Image.BICUBIC` filters was based on affine transformations
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
:py:attr:`~PIL.Image.BICUBIC`). This gave an unsatisfied result for downscaling.
At the same time high quality convolutions-based algorithm with flexible kernel
was used for :py:attr:`~PIL.Image.ANTIALIAS` filter).
Starting from 2.7 high quality convolutions-based algorithm is used for all of
these three filters.
If you have previously used any tricks to maintain quality when downscaling with
:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters
(for example, reducing within several steps), they a unnecessary now.
Antialias renamed to Lanczos
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
New :py:attr:`PIL.Image.LANCZOS` constant was added instead of
:py:attr:`~PIL.Image.ANTIALIAS`.
When :py:attr:`~PIL.Image.ANTIALIAS` was initially added, it was the only
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
are antialias from now. And the real name of :py:attr:`~PIL.Image.ANTIALIAS`
filter is Lanczos filter.
:py:attr:`~PIL.Image.ANTIALIAS` constant is leaved for backward compatibility
and is an alias for :py:attr:`~PIL.Image.LANCZOS`.
Lanczos upscaling quality
^^^^^^^^^^^^^^^^^^^^^^^^^
Image upscaling quality with :py:attr:`~PIL.Image.LANCZOS` filter was almost
the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This was fixed.
Bicubic upscaling quality
^^^^^^^^^^^^^^^^^^^^^^^^^
:py:attr:`~PIL.Image.BICUBIC` filter for affine transformations produced
sharp, slightly pixelated image for upscaling. Bicubic for convolutions is
more soft.
Resize performance
^^^^^^^^^^^^^^^^^^
In most cases convolution is more expensive algorithm for downscaling because
it takes in account all pixels of source image. Therefore
:py:attr:`~PIL.Image.BILINEAR` and :py:attr:`~PIL.Image.BICUBIC` filters
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.NEAREST`. So if such quality is suitable for your task
you can switch to :py:attr:`~PIL.Image.NEAREST` filter for downscaling,
that will give huge win in performance.
At the same time performance of convolution resampling for downscaling was
improved in about two times compared to previous version.
Upscaling performance of :py:attr:`~PIL.Image.LANCZOS` filter remained the same.
For :py:attr:`~PIL.Image.BILINEAR` filter it grew in 1.5 times and
for :py:attr:`~PIL.Image.BICUBIC` in 4 times.
Default filter for thumbnails
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In Pillow 2.5 default filter for :py:meth:`~PIL.Image.Image.thumbnail` was
changed from :py:attr:`~PIL.Image.NEAREST` to :py:attr:`~PIL.Image.ANTIALIAS`.
Antialias was chosen because all other filters gave poor quality for reduction.
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.ANTIALIAS` doesn't give any advantages after
downscaling with libJPEG, which uses supersampling internally, not convolutions.
Image transposing
-----------------
New method :py:attr:`PIL.Image.TRANSPOSE` was added for
:py:meth:`~PIL.Image.Image.transpose` operation in addition to
:py:attr:`~PIL.Image.FLIP_LEFT_RIGHT`, :py:attr:`~PIL.Image.FLIP_TOP_BOTTOM`,
:py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_180`,
:py:attr:`~PIL.Image.ROTATE_270`. :py:attr:`~PIL.Image.TRANSPOSE` is algebra
transpose, when image reflected over its main diagonal.
Speed of :py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_270`
and :py:attr:`~PIL.Image.TRANSPOSE` was significantly improved for large images,
which doesn't fit in processor cache.
Gaussian blur and unsharp mask
------------------------------
:py:meth:`~PIL.ImageFilter.GaussianBlur` implementation was replaced with
sequential applying of series of box filters. New implementation is based on
"Theoretical foundations of Gaussian convolution by extended box filtering" from
Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
implementations uses Gaussian blur internally, all changes from this chapter
also applicable to it.
Blur radius
^^^^^^^^^^^
There was an error in previous version of PIL, when blur radius (the standard
deviation of Gaussian) is actually meant blur diameter.
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
is the same as in other software.
If you used a Gaussian blur with some radius value, you need to divide this
value by two.
Blur Performance
^^^^^^^^^^^^^^^^
Box filter computation time is constant relative to the radius and depends
on source image size only. Because new Gaussian blur implementation
is based on box filter, it's computation time is also doesn't depends on blur
radius.
If before execution time for the same test image was 1 second for radius 1,
3.6 seconds for radius 10, 17 seconds for 50. Now blur with any radius on same
image is executed for 0.2 seconds.
Blur quality
^^^^^^^^^^^^
Previous implementation takes in account only source pixels within
2 * standard deviation radius for every destination pixel. This was not enough,
so quality was worse compared to other Gaussian blur software.
The new implementation does not have this drawback.

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.. toctree::
:maxdepth: 2
2.7
2.7.0