Stochastic halftone screening method
A method of building threshold matrices for stochastic screening by using the actual size and shape of imaged pixel and/or pixel agglomerate instead of the nominal one, in the stage of calculating appropriate threshold matrices. For each relevant combination of imager and imaged media, a matrix with desirable blue-noise-like characteristics is created from actual imaged pixels and/or pixel agglomerates, taking into account their overlap when placed in close proximity.
1. A method of creating a threshold matrix for stochastic screening, comprising the steps of:
providing a digital halftone image representation;
printing said halftone image;
obtaining dot-gain measurements of pixels of said printed image;
using said obtained dot-gain measurements for creating an improved threshold matrix; and
wherein said step of obtaining dot-gain measurements comprises obtaining dot-gain measurements of pixel agglomerates.
2. A method of creating a threshold matrix for stochastic screening for an initial target gray level, comprising the steps of:
a. providing an initial threshold matrix;
b. providing a merit function;
c. providing a geometrical function;
d. calculating the value of said merit function for all non-filled pixels in said matrix;
e. filling one of said pixels for which the value of said merit function is highest;
f. updating values of all pixels in said matrix adjacent to said filled pixel according to said geometrical function;
g. calculating effective percentage of surface coverage in said matrix;
h. comparing said calculated effective coverage with said target gray level;
i. repeating steps (d) through (h) until said effective coverage is greater or equal to said target gray level; and
j. storing said matrix.
3. The method of claim 2 , additionally comprising the steps of:
providing said stored matrix;
providing a new target gray level, said new target gray level higher than said initial target gray level; and
performing said steps (d) through (i).
4. A method of creating a threshold matrix for stochastic screening for an initial target gray level, comprising the steps of:
a. providing a threshold matrix representing a nominal screen pattern for said target gray level;
b. providing a merit function;
c. providing a geometrical function;
d. updating values of all non-filled pixels in said matrix according to said geometrical function;
e. calculating a value M 1 of said merit function for all filled pixels in said matrix;
f. calculating a value M 2 of said merit function for all non-filled pixels in said matrix;
g. calculating a global value G 1 for said merit function for all pixels in matrix;
h. swapping values of pixels with highest M 1 and M 2 values, respectively;
i. updating values of all non-filled pixels in said matrix affected by said swapping according to said geometrical function;
j. calculating a global value G 2 of said merit function for all pixels in said matrix;
k. comparing G 1 with G 2 ;
l. repeating said steps (e) through (k) until said global value G 2 is smaller than said global value G 1 ;
m. restoring said swapped values;
n. calculating effective percentage of surface coverage in said matrix; and
o. storing said matrix.
5. The method of claim 4 , additionally comprising the steps of:
p. providing said stored matrix;
q. providing a new target gray level, said new target gray level higher than said initial target gray level;
r. calculating a value M 2 of said merit function for all non-filled pixels in said matrix;
s. filling one of said pixels for which said merit function is highest;
t. updating values of all non-filled pixels in said matrix adjacent to said filled pixel according to said geometrical function;
u. calculating effective percentage of surface coverage in said matrix;
v. comparing said calculated effective coverage with said new target gray level;
w. repeating steps (r) through (v) until said effective coverage is greater or equal to said new target gray level; and
x. storing said matrix.
6. The method of claim 4 , additionally comprising the steps of:
p. providing said stored matrix;
q. providing a new target gray level, said new target gray level lower than said initial target gray level;
r. calculating a value M 1 of said merit function for all filled pixels in said matrix;
s. removing one of said pixels for which said value M 1 is highest;
t. updating values of all non-filled pixels in said matrix adjacent to said removed pixel according to said geometrical function;
u. calculating effective percentage of surface coverage in said matrix;
v. comparing said calculated effective coverage with said new target gray level;
w. repeating steps (r) through (v) until said effective coverage is greater or equal to said new target gray level; and
x. storing said matrix.
7. The method according to any one of claims 2 - 6 , wherein said merit function represents dot-gain of pixels and/or pixel agglomerates.
8. The method according to any one of claims 2 - 6 , wherein said geometrical function represents halftone dot shapes.
9. The method of claim 8 , wherein said geometrical function is a square.
10. The method of claim 8 , wherein said geometrical function is a circle.