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Matrix Operations for Image Processing
Paul Haeberli
Nov 1993
Introduction
Four by four matrices are commonly used to transform geometry for 3D
rendering. These matrices may also be used to transform RGB colors,
to scale RGB colors, and to control hue, saturation and contrast. The
most important advantage of using matrices is that any number of
color transformations can be composed using standard matrix
multiplication.
Please note that for these operations to be correct, we really must
operate on linear brightness values. If the input image is in a
nonlinear brightness space RGB colors must be transformed into a
linear space before these matrix operations are used.
Color Transformation
RGB colors are transformed by a four by four matrix as shown here:
xformrgb(mat,r,g,b,tr,tg,tb)
float mat[4][4];
float r,g,b;
float *tr,*tg,*tb;
{
*tr = r*mat[0][0] + g*mat[1][0] +
b*mat[2][0] + mat[3][0];
*tg = r*mat[0][1] + g*mat[1][1] +
b*mat[2][1] + mat[3][1];
*tb = r*mat[0][2] + g*mat[1][2] +
b*mat[2][2] + mat[3][2];
}
The Identity
This is the identity matrix:
float mat[4][4] = {
1.0, 0.0, 0.0, 0.0,
0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 1.0, 0.0,
0.0, 0.0, 0.0, 1.0,
};
Transforming colors by the identity matrix will leave them unchanged.
Changing Brightness
To scale RGB colors a matrix like this is used:
float mat[4][4] = {
rscale, 0.0, 0.0, 0.0,
0.0, gscale, 0.0, 0.0,
0.0, 0.0, bscale, 0.0,
0.0, 0.0, 0.0, 1.0,
};
Where rscale, gscale, and bscale specify how much to scale the r, g,
and b components of colors. This can be used to alter the color
balance of an image.
In effect, this calculates:
tr = r*rscale;
tg = g*gscale;
tb = b*bscale;
Modifying Saturation
Converting to Luminance
To convert a color image into a black and white image, this matrix is
used:
float mat[4][4] = {
rwgt, rwgt, rwgt, 0.0,
gwgt, gwgt, gwgt, 0.0,
bwgt, bwgt, bwgt, 0.0,
0.0, 0.0, 0.0, 1.0,
};
Where rwgt is 0.3086, gwgt is 0.6094, and bwgt is 0.0820. This is the
luminance vector. Notice here that we do not use the standard NTSC
weights of 0.299, 0.587, and 0.114. The NTSC weights are only
applicable to RGB colors in a gamma 2.2 color space. For linear RGB
colors the values above are better.
In effect, this calculates:
tr = r*rwgt + g*gwgt + b*bwgt;
tg = r*rwgt + g*gwgt + b*bwgt;
tb = r*rwgt + g*gwgt + b*bwgt;
Modifying Saturation
To saturate RGB colors, this matrix is used:
float mat[4][4] = {
a, b, c, 0.0,
d, e, f, 0.0,
g, h, i, 0.0,
0.0, 0.0, 0.0, 1.0,
};
Where the constants are derived from the saturation value s as shown
below:
a = (1.0s)*rwgt + s;
b = (1.0s)*rwgt;
c = (1.0s)*rwgt;
d = (1.0s)*gwgt;
e = (1.0s)*gwgt + s;
f = (1.0s)*gwgt;
g = (1.0s)*bwgt;
h = (1.0s)*bwgt;
i = (1.0s)*bwgt + s;
One nice property of this saturation matrix is that the luminance of
input RGB colors is maintained. This matrix can also be used to
complement the colors in an image by specifying a saturation value of
1.0.
Notice that when s is set to 0.0, the matrix is exactly the "convert
to luminance" matrix described above. When s is set to 1.0 the matrix
becomes the identity. All saturation matrices can be derived by
interpolating between or extrapolating beyond these two matrices.
This is discussed in more detail in the note on Image Processing By
Interpolation and Extrapolation.
Applying Offsets to Color Components
To offset the r, g, and b components of colors in an image this
matrix is used:
float mat[4][4] = {
1.0, 0.0, 0.0, 0.0,
0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 1.0, 0.0,
roffset,goffset,boffset,1.0,
};
This can be used along with color scaling to alter the contrast of
RGB images.
Simple Hue Rotation
To rotate the hue, we perform a 3D rotation of RGB colors about the
diagonal vector [1.0 1.0 1.0]. The transformation matrix is derived
as shown here:
If we have functions:
identmat(mat)
that creates an identity matrix.
xrotatemat(mat,rsin,rcos)
that multiplies a matrix that rotates about the x (red) axis.
yrotatemat(mat,rsin,rcos)
that multiplies a matrix that rotates about the y (green) axis.
zrotatemat(mat,rsin,rcos)
that multiplies a matrix that rotates about the z (blue) axis.
Then a matrix that rotates about the 1.0,1.0,1.0 diagonal can be
constructed like this:
First we make an identity matrix
identmat(mat);
Rotate the grey vector into positive Z
mag = sqrt(2.0);
xrs = 1.0/mag;
xrc = 1.0/mag;
xrotatemat(mat,xrs,xrc);
mag = sqrt(3.0);
yrs = 1.0/mag;
yrc = sqrt(2.0)/mag;
yrotatemat(mat,yrs,yrc);
Rotate the hue
zrs = sin(rot*PI/180.0);
zrc = cos(rot*PI/180.0);
zrotatemat(mat,zrs,zrc);
Rotate the grey vector back into place
yrotatemat(mat,yrs,yrc);
xrotatemat(mat,xrs,xrc);
The resulting matrix will rotate the hue of the input RGB colors. A
rotation of 120.0 degrees will exactly map Red into Green, Green into
Blue and Blue into Red. This transformation has one problem, however,
the luminance of the input colors is not preserved. This can be fixed
with the following refinement:
Hue Rotation While Preserving Luminance
We make an identity matrix
identmat(mmat);
Rotate the grey vector into positive Z
mag = sqrt(2.0);
xrs = 1.0/mag;
xrc = 1.0/mag;
xrotatemat(mmat,xrs,xrc);
mag = sqrt(3.0);
yrs = 1.0/mag;
yrc = sqrt(2.0)/mag;
yrotatemat(mmat,yrs,yrc);
matrixmult(mmat,mat,mat);
Shear the space to make the luminance plane horizontal
xformrgb(mmat,rwgt,gwgt,bwgt,&lx,&ly,&lz);
zsx = lx/lz;
zsy = ly/lz;
zshearmat(mat,zsx,zsy);
Rotate the hue
zrs = sin(rot*PI/180.0);
zrc = cos(rot*PI/180.0);
zrotatemat(mat,zrs,zrc);
Unshear the space to put the luminance plane back
zshearmat(mat,zsx,zsy);
Rotate the grey vector back into place
yrotatemat(mat,yrs,yrc);
xrotatemat(mat,xrs,xrc);
Conclusion
I've presented several matrix transformations that may be applied to
RGB colors. Each color transformation is represented by a 4 by 4
matrix, similar to matrices commonly used to transform 3D geometry.
Example C code that demonstrates these concepts is provided for your
enjoyment.
These transformations allow us to adjust image contrast, brightness,
hue and saturation individually. In addition, color matrix
transformations concatenate in a way similar to geometric
transformations. Any sequence of operations can be combined into a
single matrix using matrix multiplication.
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