[X,map] = rgb2ind(RGB,n) converts
the RGB image to an indexed image X using minimum
variance quantization and dithering. map contains
at most n colors. n must be
less than or equal to 65,536.

X = rgb2ind(RGB, map) converts
the RGB image to an indexed image X with colormap map using
the inverse colormap algorithm and dithering. size(map,1) must
be less than or equal to 65,536.

[X,map] = rgb2ind(RGB, tol) converts
the RGB image to an indexed image X using uniform
quantization and dithering. map contains at most (floor(1/tol)+1)^3 colors. tol must
be between 0.0 and 1.0.

[___] = rgb2ind(___,dither_option) enables
or disables dithering. dither_option is a string
that can have one of these values.

'dither' (default)

Dithers, if necessary, to achieve better color resolution
at the expense of spatial resolution

'nodither'

Maps each color in the original image to the closest
color in the new map. No dithering is performed.

Note
The values in the resultant image X are indexes
into the colormap map and should not be used in
mathematical processing, such as filtering operations.

Class Support

The input image can be of class uint8, uint16, single,
or double. If the length of map is
less than or equal to 256, the output image is of class uint8.
Otherwise, the output image is of class uint16.

The value 0 in the output array X corresponds
to the first color in the colormap.

Examples

Read and display a truecolor uint8 JPEG image
of a nebula.

If you specify tol, rgb2ind uses
uniform quantization to convert the image. This method involves cutting
the RGB color cube into smaller cubes of length tol.

If you specify n, rgb2ind uses
minimum variance quantization. This method involves cutting the RGB
color cube into smaller boxes (not necessarily cubes) of different
sizes, depending on how the colors are distributed in the image. If
the input image actually uses fewer colors than the number you specify,
the output colormap is also smaller.

If you specify map, rgb2ind uses
colormap mapping, which involves finding the colors in map that
best match the colors in the RGB image.

Uniform Quantization —
Uniform quantization cuts the RGB color cube into smaller cubes of
length tol. For example, if you specify a tol of
0.1, the edges of the cubes are one-tenth the length of the RGB cube.
The total number of small cubes is:

n = (floor(1/tol)+1)^3

Each cube represents a single color in the output image. Therefore,
the maximum length of the colormap is n. rgb2ind removes
any colors that don't appear in the input image, so the actual
colormap can be much smaller than n.

Minimum Variance Quantization —
Minimum variance quantization cuts the RGB color cube into smaller
boxes (not necessarily cubes) of different sizes, depending on how
the colors are distributed in the image. If the input image actually
uses fewer colors than the number specified, the output colormap is
also smaller.

Inverse Colormap — The
inverse colormap algorithm quantizes the specified colormap into 32
distinct levels per color component. Then, for each pixel in the input
image, the closest color in the quantized colormap is found.

References

[1] Spencer W. Thomas, "Efficient Inverse Color Map Computation", Graphics
Gems II, (ed. James Arvo), Academic Press: Boston. 1991.
(includes source code)