2.7.0

Sane Plugin

The Sane plugin has now been split into its own repo: https://github.com/python-pillow/Sane .

Png text chunk size limits

To prevent potential denial of service attacks using compressed text chunks, there are now limits to the decompressed size of text chunks decoded from PNG images. If the limits are exceeded when opening a PNG image a ValueError will be raised.

Individual text chunks are limited to PIL.PngImagePlugin.MAX_TEXT_CHUNK, set to 1MB by default. The total decompressed size of all text chunks is limited to PIL.PngImagePlugin.MAX_TEXT_MEMORY, which defaults to 64MB. These values can be changed prior to opening PNG images if you know that there are large text blocks that are desired.

Image resizing filters

Image resizing methods resize() and thumbnail() take a resample argument, which tells which filter should be used for resampling. Possible values are: PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC and PIL.Image.ANTIALIAS. Almost all of them were changed in this version.

Bicubic and bilinear downscaling

From the beginning BILINEAR and 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 BILINEAR and 4x4 for BICUBIC). This gave an unsatisfactory result for downscaling. At the same time, a high quality convolutions-based algorithm with flexible kernel was used for 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 BILINEAR and BICUBIC filters (for example, reducing within several steps), they are unnecessary now.

Antialias renamed to Lanczos

A new PIL.Image.LANCZOS constant was added instead of ANTIALIAS.

When 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 ANTIALIAS filter is Lanczos filter.

The ANTIALIAS constant is left for backward compatibility and is an alias for LANCZOS.

Lanczos upscaling quality

The image upscaling quality with LANCZOS filter was almost the same as BILINEAR due to bug. This has been fixed.

Bicubic upscaling quality

The 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 BILINEAR and BICUBIC filters’ performance can be lower than before. On the other hand the quality of BILINEAR and BICUBIC was close to NEAREST. So if such quality is suitable for your tasks you can switch to 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 LANCZOS filter has remained the same. For BILINEAR filter it has improved by 1.5 times and for BICUBIC by four times.

Default filter for thumbnails

In Pillow 2.5 the default filter for thumbnail() was changed from NEAREST to ANTIALIAS. Antialias was chosen because all the other filters gave poor quality for reduction. Starting from Pillow 2.7.0, ANTIALIAS has been replaced with BICUBIC, because it’s faster and ANTIALIAS doesn’t give any advantages after downscaling with libjpeg, which uses supersampling internally, not convolutions.

Image transposition

A new method PIL.Image.TRANSPOSE has been added for the transpose() operation in addition to FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM, ROTATE_90, ROTATE_180, ROTATE_270. TRANSPOSE is an algebra transpose, with an image reflected across its main diagonal.

The speed of ROTATE_90, ROTATE_270 and TRANSPOSE has been significantly improved for large images which don’t fit in the processor cache.

Gaussian blur and unsharp mask

The 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 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 depend 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.

TIFF Parameter Changes

Several kwarg parameters for saving TIFF images were previously specified as strings with included spaces (e.g. ‘x resolution’). This was difficult to use as kwargs without constructing and passing a dictionary. These parameters now use the underscore character instead of space. (e.g. ‘x_resolution’)