Hi Marty,

A picture is data from a sensor. In this case, the sensor is a camera. 
The sensor is far from perfect. A picture that is supposed to have 3 
colors could easily be more than 15,000 colors. The data that has value 
from any picture is call the signal and everything else is called noise. 
For example, if we are trying to see an animal in the forest, the dots 
belonging to the animal contain the data (and a lot of noise), and all 
other dots are called noise. I'm not speaking down to you, I'm just 
giving you some background information.

Color grouping (aka color segmentation) and shape grouping are noise 
suppression techniques that eliminate most of the noise in an image. 
When the noise is reduced, SVG becomes much more efficient.

No information (signal) can be extracted from a picture unless two or 
more dots are constellated into a group. In graphics (from machine 
vision I think), this is called a blob.

It is immaterial whether the dots in a blob have different colors. As 
humans, we group by colors. In other words, if the data is not 
constellated by color, then we can't see it. Each group has one color. 
In SVG, a path is closed around a blob and filled with a single color.

When a photograph is converted to SVG, everyone does color grouping. 
Most color groupers rely on frequency analysis or averaging techniques. 
These techniques produce file sizes that are about 15 times larger than 
a JPG, and they look much worse. It is all about signal to noise 
extraction. At our company, we use a much more involved color grouping 
process, and for that effort we can color group well enough that our 
files are about 1.5 times larger than SVG and they are somewhat 
comparable to JPG.

We haven't added shape grouping, yet, but that would reduce the noise 
further which would allow us to produce SVG which has a smaller file 
size than JPG.

Shape grouping is explained in my previous E-mail. When shape grouping 
uses geographic shapes as the objective (we call this entropic 
compression), then we can do about 10 times better than JPG, and the 
quality is higher than JPG.

More compression can be obtained by organizing the mathematical 
representation, but I will leave that for later.






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