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WHAT IS FARBFELD?
Farbfeld is a lossless image-format designed to be
parsed and piped easily. It is probably the simplest
image-format you can find (see FORMAT).
It does not have integrated compression, but allows
compression algorithms to work with it easily by adding
little entropy to the image data itself. This beats PNG
in many cases.
Given the free choice of compression algorithms, it
is trivial to switch to better and faster ones as they
show up in the future.
HOW DO I USE THE TOOLS?
encoding:
png2ff < example.png > example.ff
png2ff < example.png | bzip2 > example.ff.bz2
decoding:
ff2png < example.ff > example.png
bzcat example.ff.bz2 | ff2png > example.png
bzip2 is used in this example and a recommended
compression algorithm. Of course you are free
to use something else.
WHY FARBFELD?
Current image-formats have integrated compression,
making it complicated to read the image data.
One is forced to use complex libraries like libpng,
libjpeg, libjpeg-turbo, giflib and others, read the
documentation and write a lot of boilerplate in order
to get started.
Farbfeld leaves this behind and is designed to be as
simple as possible, leaving the task of compression
to outside tools.
The simple design, which was the primary objective,
implicitly lead to the very good compression
characteristics, as it often happens when you go with
the UNIX philosophy.
Reading farbfeld images doesn't require any special
libraries. The tools in this folder are just a toolbox
to make it easy to convert between common image formats
and farbfeld.
HOW DOES IT WORK?
In farbfeld, pattern resolution is not done while
converting, but while compressing the image.
For example, farbfeld always stores the alpha-channel,
even if the image doesn't have alpha-variation.
This may sound like a big waste at first, but as
soon as you compress an image of this kind, the
compression-algorithm (e.g. bzip2) recognizes the
pattern that every 48 bits the 16 bits store the
same information.
And the compression-algorithms get better and better
at this.
Same applies to the idea of having 16 bits per channel.
It sounds excessive, but if you for instance only have
a greyscale image, the R, G and B channels will store
the same value, which is recognized by the compression
algorithm easily.
This effectively leads to filesizes you'd normally only
reach with paletted images, and in some cases bzip2 even
beats png's compression, for instance when you're dealing
with grayscale data, line drawings, decals and even
photographs.
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