I’ve been steadily working on my wavelet image compression for the past few weeks, and in the process have improved it in many ways. These are largely not technical improvements, but rather a huge code refactoring, the creation of decent documentation, reducing memory usage an so on.
You can read the freshly pressed documentation or simply download the source.
It is a fairly simple but thus compact (executable with compression and decompressing is 30kb uncompressed) and relatively speedy image compression library, that provides up to 16 channels per file and combines lossless with lossy compression in a single algorithm, that can even be changed from channel to channel. As it’s based on the wavelet transform, it allows for progressive decoding (which means that if you only have the beginning of the file, you get a lower quality version of the whole file) and can also extract smaller “thumbnails” of the original image.
For encoding it also support various modes, one is to give a mean-square error for each channel (similar to the JPEG quality setting), and another one is to fit the best quality image into a given amount of bits.
Unfortunately, there is a catch with the new version, too (and this is the reason why the sidebar link still refers to the old version). As my primary development platform has moved from Windows to Mac OS (and Linux), I have not updated the Windows GUI (written in Delphi) nor the Web-Browser plugin. I plan to offer new GUIs eventually; the current plan is to write one in C# for Windows and Linux, and do a native Cocoa one for Mac OS.
Finally, I’ve changed the license from the GPL to the zlib-license, which should allow use in closed source applications. If you decide to use it, or even decide not to use it, feedback and suggestions would be much appreciated.
code
- bitbucket repositories
- Wavelet Library 3 Newer version of the lossy and lossless, completely embedded image compression library (under zlib-License)
- WowPlot Graphical analysis tool for World of Warcraft combat logs for Mac OS X Leopard (10.5). Its main focus lies in evaluating time-dependant combat performance in a very free-form fashion.
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