Electrophotographic printing system including lateral translations to reduce burn-in artifacts

1. A method for reducing image burn-in artifacts in an electrophotographic printing system, comprising:

receiving a print job including image data for a set of pages to be printed with the electrophotographic printing system;

determining that the image data for a sequence of pages in the print job are similar; and

determining printing data to enable a print engine to print the similar pages using a pattern of lateral translations wherein the image data is laterally translated such that it is printed at a different lateral position on the printed page.

2. The method of claim 1 , wherein the pattern of lateral translations includes a pattern of cross-track translations.

3. The method of claim 1 , wherein the pattern of lateral translations includes a pattern of in-track translations.

4. The method of claim 1 , wherein the pattern of lateral translations is a periodic pattern.

5. The method of claim 1 , wherein the pattern of lateral translations is a random or pseudo-random pattern.

6. The method of claim 1 , wherein the image data for a sequence of pages are determined to be similar based on metadata associated with the print job indicating that the print job includes multiple copies of the same page having the same image data.

7. The method of claim 1 , wherein the image data for a sequence of pages are determined to be similar by automatically analyzing the image data to compute a predefined image similarity metric, and designating sequential pages to be similar by comparing the image similarity metric to a predefined threshold.

8. The method of claim 7 , wherein computing the image similarity metric includes:

computing a cross-track image profile from the image data for sequential pages;

computing image difference statistics from the cross-track image profiles; and

computing the image similarity metric responsive to the image difference statistics.

9. The method of claim 8 , wherein the image difference statistics include a mean absolute difference between the cross-track image profiles, an RMS difference between the cross-track image profiles, a standard deviation of the difference between the cross-track image profiles or a variance of the difference between the cross-track image profiles.

10. The method of claim 7 , wherein computing the image similarity metric includes:

computing cross-track image profiles {circumflex over (P)} i (x) from the image data I i (x,y) for a set of sequential pages, where:

P

^

i

(

x

)

=

1

N

y

⁢

∑

y

=

1

N

y

I

i

(

x

,

y

)

-

p

0

,

i is the image number, x and y are the cross-track and in-track pixel positions, respectively, Ny is the number of rows in the image data, and p0 is the midpoint of the code value range for the image data; determining an average cross-track profile B i (x) by averaging the cross-track image profiles for the set of K sequential pages using the equation, where:

B

i

(

x

)

=

1

K

⁢

∑

k

=

0

K

-

1

P

^

(

i

-

k

)

(

x

)

,

and K is the number of pages being averaged; computing a local range of the average cross-track image profile in a window of cross-track positions as a function of cross-track position, where:

R

i

(

x

)

=

max

x

j

(

B

i

(

x

j

)

)

-

min

x

j

(

B

i

(

x

j

)

)

,

the max(⋅) and min(⋅) functions return the maximum and minimum values, respectively, of the average cross-track profile in a window of cross-track positions x j given by (x−W/2)≤x j ≤(x+W/2);

computing the image similarity metric S i by determining a maximum of the local range of the average cross-track image profile, where

S

i

=

max

x

(

R

i

(

x

)

)

and the max(⋅) function determines the maximum value across the range of x values.