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docs/releasenotes/2.7.0.rst

@@ -1,144 +1,143 @@
-Pillow 2.7
+Pillow 2.7.0
-==========
+============
 
 Image resizing filters
 ----------------------
 
 Image resizing methods :py:meth:`~PIL.Image.Image.resize` and
-:py:meth:`~PIL.Image.Image.thumbnail` takes `resample` argument, which tells
+:py:meth:`~PIL.Image.Image.thumbnail` take a `resample` argument, which tells
-what filter should be used for resampling. Possible values are:
+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 was changed in this version.
+Almost all of them were changed in this version.
 
-Bicubic and Bilinear downscaling
+Bicubic and bilinear downscaling
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-From very begining :py:attr:`~PIL.Image.BILINEAR` and
+From the beginning :py:attr:`~PIL.Image.BILINEAR` and
-:py:attr:`~PIL.Image.BICUBIC` filters was based on affine transformations
+:py:attr:`~PIL.Image.BICUBIC` filters were based on affine transformations
-and uses fixed number of pixels from source image for every destination pixel
+and used a fixed number of pixels from the source image for every destination
-(that was 2x2 pixels for :py:attr:`~PIL.Image.BILINEAR` and 4x4 for
+pixel (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 unsatisfactory result for
-At the same time high quality convolutions-based algorithm with flexible kernel
+downscaling. At the same time, a high quality convolutions-based algorithm with
-was used for :py:attr:`~PIL.Image.ANTIALIAS` filter).
+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
+Starting from Pillow 2.7.0, a high quality convolutions-based algorithm is used
-these three filters.
+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.
+(for example, reducing within several steps), they are unnecessary now.
 
 Antialias renamed to Lanczos
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-New :py:attr:`PIL.Image.LANCZOS` constant was added instead of 
+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 2.7 all resize method are based on convolutions. All of them
+this. Starting from Pillow 2.7.0 all resize method are based on convolutions.
-are antialias from now. And the real name of :py:attr:`~PIL.Image.ANTIALIAS`
+All of them are antialias from now on. And the real name of the
-filter is Lanczos filter.
+:py:attr:`~PIL.Image.ANTIALIAS` filter is Lanczos filter.
 
-:py:attr:`~PIL.Image.ANTIALIAS` constant is leaved for backward compatibility
+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
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Image upscaling quality with :py:attr:`~PIL.Image.LANCZOS` filter was almost
+The image upscaling quality with :py:attr:`~PIL.Image.LANCZOS` filter was
-the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This was fixed.
+almost the same as :py:attr:`~PIL.Image.BILINEAR` due to bug. This has been fixed.
 
 Bicubic upscaling quality
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-:py:attr:`~PIL.Image.BICUBIC` filter for affine transformations produced
+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 expensive algorithm for downscaling because
+In most cases, convolution is more a expensive algorithm for downscaling
-it takes in account all pixels of source image. Therefore 
+because it takes into account all the 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 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 task
+: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,
-that will give huge win in performance.
+which will give a huge improvement in performance.
 
-At the same time performance of convolution resampling for downscaling was
+At the same time performance of convolution resampling for downscaling has been
-improved in about two times compared to previous version.
+improved by around a factor of two compared to the previous version.
-Upscaling performance of :py:attr:`~PIL.Image.LANCZOS` filter remained the same.
+The upscaling performance of the :py:attr:`~PIL.Image.LANCZOS` filter has
-For :py:attr:`~PIL.Image.BILINEAR` filter it grew in 1.5 times and
+remained the same. For :py:attr:`~PIL.Image.BILINEAR` filter it has improved by
-for :py:attr:`~PIL.Image.BICUBIC` in 4 times.
+1.5 times and for :py:attr:`~PIL.Image.BICUBIC` by four times.
 
 Default filter for thumbnails
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-In Pillow 2.5 default filter for :py:meth:`~PIL.Image.Image.thumbnail` was
+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 other filters gave poor quality for reduction.
+Antialias was chosen because all the other filters gave poor quality for
-Starting from Pillow 2.7 :py:attr:`~PIL.Image.ANTIALIAS` replaced with
+reduction. Starting from Pillow 2.7.0, :py:attr:`~PIL.Image.ANTIALIAS` has been
-:py:attr:`~PIL.Image.BICUBIC`, because it faster and
+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.
+downscaling with libjpeg, which uses supersampling internally, not convolutions.
 
-Image transposing
+Image transposition
------------------
+-------------------
 
-New method :py:attr:`PIL.Image.TRANSPOSE` was added for
+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 algebra
+:py:attr:`~PIL.Image.ROTATE_270`. :py:attr:`~PIL.Image.TRANSPOSE` is an algebra
-transpose, when image reflected over its main diagonal.
+transpose, with an image reflected across its main diagonal.
 
-Speed of :py:attr:`~PIL.Image.ROTATE_90`, :py:attr:`~PIL.Image.ROTATE_270`
+The 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,
+and :py:attr:`~PIL.Image.TRANSPOSE` has been significantly improved for large
-which doesn't fit in processor cache.
+images which don't fit in the processor cache.
 
 Gaussian blur and unsharp mask
 ------------------------------
 
-:py:meth:`~PIL.ImageFilter.GaussianBlur` implementation was replaced with
+The :py:meth:`~PIL.ImageFilter.GaussianBlur` implementation has been replaced
-sequential applying of series of box filters. New implementation is based on
+with a sequential application of box filters. The 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`
+the Mathematical Image Analysis Group. As :py:meth:`~PIL.ImageFilter.UnsharpMask`
-implementations uses Gaussian blur internally, all changes from this chapter
+implementations use Gaussian blur internally, all changes from this chapter
-also applicable to it.
+are also applicable to it.
 
 Blur radius
 ^^^^^^^^^^^
 
-There was an error in previous version of PIL, when blur radius (the standard
+There was an error in the previous version of Pillow, where blur radius (the
-deviation of Gaussian) is actually meant blur diameter.
+standard deviation of Gaussian) actually meant blur diameter. For example, to
-For example for blurring image with actual radius 5 you were forced
+blur an image with actual radius 5 you were forced to use value 10. This has
-to use value 10. This was fixed. For now the meaning of the radius
+been fixed. 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 divide this
 value by two.
 
-Blur Performance
+Blur performance
 ^^^^^^^^^^^^^^^^
 
 Box filter computation time is constant relative to the radius and depends
-on source image size only. Because new Gaussian blur implementation
+on source image size only. Because the new Gaussian blur implementation
-is based on box filter, it's computation time is also doesn't depends on blur
+is based on box filter, its computation time also doesn't depends on the blur
 radius.
 
-If before execution time for the same test image was 1 second for radius 1,
+For example, previously, if the execution time for a given test image was 1
-3.6 seconds for radius 10, 17 seconds for 50. Now blur with any radius on same
+second for radius 1, 3.6 seconds for radius 10 and 17 seconds for 50, now blur
-image is executed for 0.2 seconds.
+with any radius on same image is executed for 0.2 seconds.
 
 Blur quality
 ^^^^^^^^^^^^
 
-Previous implementation takes in account only source pixels within
+The previous implementation takes into 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.
+so the quality was worse compared to other Gaussian blur software.
 
 The new implementation does not have this drawback.