Popularity Algorithms
Popularity algorithms are another form of uniform
quantization. However, instead of dividing the color space into 256
regions these algorithms break the color space into much smaller, and
consequently many more, regions. One possible implementation is to
divide the space into regions 4x4x4 in size (262,144 regions). The
original colors are again mapped to the region they fall in. The
representative color for each region is the average of the colors
mapped to it. The color map is selected by taking the representative
colors of the 256 most popular regions (the regions that had the most
colors mapped to them). If a non-empty region is not selected for the
color map its index into the color map (the index that will be
assigned to colors that map to that region) is then the entry in the
color map that is closest (Euclidean distance) to its representative
color).
These algorithms are also easy to implement and yield better results
than the uniform quantization algorithm. They do,
however, take slightly longer to execute and can have a significantly
larger storage requirement depending on the size of regions. Also
depending on the image characteristics this may not produce a good
result. This can be said about all uniform sub-division schemes,
because the method for dividing the color space does utilize any
information about the image