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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.