HTTPS clone URL
Subversion checkout URL
`pngwolf` uses a genetic algorithm to find PNG scanline filter combinations that compress well
Fetching latest commit…
Cannot retrieve the latest commit at this time
|Failed to load latest commit information.|
`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 7-Zip library by Igor Pavlov 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 7-Zip 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 be- cause it uses 7-Zip's Deflate implementation (`AdvanceCOMP` uses that aswell, although an older version which sometimes performs better and sometimes worse, for reasons yet to be studied). 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 ho- listic 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 suit- able 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 op- timization (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` you need three additional libraries: * GAlib http://lancet.mit.edu/ga/dist/ * 7-Zip http://www.7-zip.org/download.html ("7-Zip Source code") * zlib http://zlib.net/ Put these into `galib`, `7zip`, and `zlib` sub-directories into the directory where pngwolf.cxx is located, and then either use the CMake utility (http://www.cmake.org/) on the `CMakeLists.txt`, or simply specify all the files specified in `CMakeLists.txt` as input to your compiler. The latter would look like: % gcc -I7zip/CPP -Igalib pngwolf.cxx galib/ga/GA1DArrayGenome.C galib/ga/GAAllele.C ... -lstdc++ -o pngwolf If you are using 7-Zip 9.20 there are two bugs in 7-Zip that prevent gcc building https://sourceforge.net/support/tracker.php?aid=3200655 it. To address that, apply the patch `sevenzip920.patch` like so: % patch -p 0 < sevenzip920.patch I've done this successfully with Visual C++ 2010 and Cygwin gcc 4.3.4.