pngwolf-zopfli is a version of pngwolf that uses Zopfli for the
final compression step.
pngwolf is a tool to minimize the size of PNG image files. There are
a number of factors that affect the size of PNG image files, such as
the number of colors in the image and whether the image data is stored
as RGBA data or in the form of references to a color palette. The main
factor is the quality of the Deflate compression used to compress the
image data, which is in turn affected by the quality of the compressor
and how well the data to be compressed is arranged.
The PNG format supports a number of scanline filters that transform the image data by relating nearby pixels mathematically. Choosing the right filters for each scanline can make the image data more compressible. It is, however, infeasible for non-trivial images to find the best filters so typical encoders rely on a couple of heuristics to find good filters.
pngwolf employs a genetic algorithm to find better filter combinations
than traditional heuristics. It derives a couple of filter combinations
heuristically, adds a couple of random combinations, and then looks how
well each combination compresses. Two very different combinations may
compress similarily well, for instance, one combination may be very good
for the first couple of scanlines, while the other may be very good for
the last couple of scanlines. So taking the beginning of one combination
and the tail of the other to make a new one may result in a combination
that compresses better then the original two.
That is, in essence, what pngwolf does, over and over again. Further,
the most widely used PNG encoders use the zlib library for compression.
The zlib library favours speed over compression ratio in some cases, so
whatever filters are selected to aid compression, the result with zlib
may not be the smallest possible. The Zopfli library has a Deflate
encoder that favours size over speed at certain settings. So, pngwolf
attempts to make use of both: a fast zlib setting is used to estimate
how well some filter combination aids compression, and when it gets
bored, it uses Zopfli to generate the final result.
Doing this pngwolf is able to compress some images better than other
optimizers (like OptiPNG, AdvanceCOMP, pngcrush, and pngout),
either because it finds better filter combinations then they do, or
because it uses Zopfli's Deflate implementation. It does not attempt to
make other optimizations, like converting indexed images to RGB format.
None of the tools mentioned, including pngwolf follow any kind of
holistic approach to PNG optimization, so to get the best results they
need to be used in combination (and sometimes applying them repeatedly
or in different orders provides the best results). As far as I can tell
most other tools do not try to preserve the filter combination in the
original image, so pngwolf should usually be used last or
second-to-last in the optimization process.
For images that are already optimized using all the other tools, there
is about 1% further reduction to be expected from pngwolf for
suitable images. Still, it should be rare to find images on the Web
that pngwolf cannot compress a little bit further.
The tool suffers from the lack of a Deflate encoder that makes it easy store the results of data analysis (where are duplicate substrings in the data) and combine them (if you recall the earlier example where it takes the head of one combination and the tail of another, an encoder would not have to analyze all of the two parts again, only where they overlap). So it can often take a long time (as in minutes) to find the best results.
Regardless of the performance deficiency pngwolf is well-suited as a
research tool to come up with better heuristics for filter selection,
or to extend the genetic algorithm approach to other aspects of PNG
optimization (the main thing being considered is re-arranging the
entries in color palettes so the image data compresses better). The
tool logs extensive information in a YAML-based machine-readable format
while it attempts to optimize images which should aid in that.
It also addresses two (other) user-interface issues I had in using the other tools, namely it allows you to make it stop trying to find better optimizations at well-specified points (such as the total time used), and if you start an optimization run but grow impatient and abort the program, results should not get lost, but should be stored anyway.
To compile pngwolf-zopfli you need four additional libraries:
Put these into galib, libdeflate, zlib, and zopfli sub-directories
into the directory where pngwolf.cxx is located.
galib247.patch fixes an issue in GAlib 2.4.7 when compiling with GCC
and Clang, and adds parallel evaluation using OpenMP.
If you cloned the git repository, you can use
git submodule update --init --recursive to retrieve these dependencies.
Use the CMake utility on the CMakeLists.txt,
use one of the supplied makefiles, or simply specify all the files
specified in CMakeLists.txt as input to your compiler.
The original pngwolf is licensed under GPLv2-or-later. The pngwolf-zopfli
version as a whole is licensed under GPLv3, to allow distribution of binaries
linked with Zopfli (which is licensed under the Apache License, Version 2.0).