HP-PURDUE-CONFIDENTIAL Final Exam May 16th 2008 Slide No.1 Outline Motivations Analytical Model of...

HP-PURDUE-CONFIDENTIALFinal Exam May 16th 2008

Slide No. 1

Outline

Motivations

Analytical Model of Skew Effect and its Compensation in

Banding and MTF Characterization

Moiré Artifact Prediction and Reduction in a Variable Data

Printing Environment

Conclusions

References


Slide No. 2

Moiré Artifacts in Printing

Moiré due to halftoning process

Test pattern used to characterizehalftoning processing of press

Example image to be printed showing moiré artifacts


Slide No. 3

Quality of Embedded Images Example: Moiré Artifact

Business Week, April 30, 2007 p.56


Slide No. 4

Document Composition Affects Artifact Perceptibility

Artifact assessment depend

on document composition: Image scaling and rotation

Image cropping

Image position relative to other objects

Background color

Object overlay on image


Slide No. 5

Causes and Difficulties to Detect Moiré Artifacts in VDP

Halftone screen pattern interacts

with digital image Clustered dot profile

Limited spatial resolution of the

digital press Typical digital press :

180 line-per-inch

In digital publishing environment

with variable data printing Inspecting each printed page is

not cost efficient

Moiré artifacts are image content

dependent

Moiré artifacts vary with the

printing device


Slide No. 6

Phases and Components of Automatic Workflow[3]


Slide No. 7

Spectrum of Halftoned Digital Image in Terms of Spectrum of Original

Continuous-tone Image Spectrum of the halftoned digital image can be expressed in terms of the original

image and the halftone screen H(u,v) -- spectrum of halftone image

f[l,k] -- original image

p[m,n;a] -- halftone dot profile

M – size of the halftone cell

1

0

1

0

1

1

0

1

0

)(2exp];,[];,[

)(2exp]],[;,[),(

,),(

2

M

m

M

nM

k lst

M

s

M

tst

M

ntmsjanmpatsP

vkuljklftsPvuF

M

tv

M

suFvuH


Slide No. 8

Illustration of Halftone Spectrum for a Sine Wave Image

Continuous-tone input image Halftone imageScreening

Compare

5 1 6 12

4 0 2 10

8 3 7 13

14 9 11 15

Threshold matrix

Spectrum of the continuous-tone input image Spectrum of the halftone image

Frequency doubling effect

Frequency of the original sinusoidal

wave


Slide No. 9

Nonlinear Transformation Due to Halftone

|P[0,0;a]| |P[0,1;a]|

|P[0,2;a]|

|P[1;a]|

f[l]

a

l

l

P[1;f[l]]

1

T

T

0

AB C

A’B’

C’

Fst (u,v) P[ s, t; f [l, k]]exp j2 (ul vk) l

k

P[s, t;a] 1

M 2 p[m,n;a]exp j2 (ms nt)

M

n0

M 1

m0

M 1


Slide No. 10

Frequency Doubling Effect Due to Nonlinear Transformation

The frequency doubling effect is due to the non-linear

transform caused by the screening process

Clustered halftone dot profile that is used in laser printing

is likely to cause this frequency doubling effect


Slide No. 11

Moiré Artifact as Result of Frequency Doubling Effect

Continuous-tone input image Halftone image

Screening

Compare

5 1 6 12

4 0 2 10

8 3 7 13

14 9 11 15

Threshold matrix

Spectrum of the continuous-tone input image Spectrum of the halftone image

Moiré artifacts as low frequency component

Frequency of the original sinusoidal

wave


Slide No. 12

Moiré Prediction

Image Database

Press Profile Detection Algorithm

Human Visual System Model

Moiré Map

Image Analysis

Test Pattern Digital Press

Real-time analysis of images in documentOffline press characterization process


Slide No. 13

Digital Press Characterization

Use Bullseye test pattern Sweep of signal at all angles

Spatial frequency at each location is proportional to its distance to the center

Bullseye test pattern is printed using target digital press

Moiré inducing frequency (MIF) generates low frequency moiré that forms secondary bullseye pattern on the print

After scanning the printout, we detect the secondary bullseye pattern to locate MIFHalftone bullseye test pattern with moiré artifacts


Slide No. 14

Moiré Inducing Frequency (MIF) Detection on Test Page

This test pattern shows multiple

moiré artifacts patterns

Each moiré artifact exhibits a

pattern of concentric circles

The xy coordinates of the center

of each pattern of concentric

circles correspond to a frequency

that may cause moiré artifacts in

the printed image

moiré artifacts


Slide No. 15

Bullseye pattern halftoned with 150 cycles/inch, 0 degree screen; printed at 600 dpi and scanned at 600 dpi. The red dots indicate detected MIF’s

Symmetry of the Secondary Bullseye Artifacts

The secondary bullseye

artifacts are symmetric to the

center of the test page

Each secondary bullseye

artifact forms concentric circles

Some pairs of secondary

bullseye artifacts that are

symmetrical to the center show

different gray levels


Slide No. 16

1-D illustration

Image: 5 cycles per inchScreen: 10 cycles per inch

Average: 0.375

Average: 0.4667

Same frequency


Slide No. 17

Anisotropy Measurements on Scanned Bullseye Pattern[4]

Each image pixel’s anisotropy

measurement is calculated based on

a disk area

Image pixels within the disk is divided

into annuli

The width of each annulus is delta, ∆

Image pixels are sorted into annulus

(bins) based on their distance to the

center of the region

Mean and variance are calculated for

each bin

Calculate Anisotropy for each bin


Slide No. 18

Modified Anisotropy Measurement Secondary Bullseye Artifacts

Modified anisotropy

measurement takes account

on the entire region’s energy

to give better distinction

between concentric circles

(secondary bullseye) and

random noise region

For k-th annulus:

Ak

1

N (k)p

pSk

, k

Vk2

Ak2

Vk2

1

N (k) 1( p A

k)2

pSk

,

For the region of N annuli:

A ppS N (k)

k 1

N

, E ( p A)2

pS

Symmetrical Value: [m,n]= E k

k=1

N

: width of each annulus

N: number of annuli in the region

p : the pixel value

Sk

: set of pixels belong to kth annulus

N (k): number of pixels in kth annulus

S : set of pixels belong to any annulus


Slide No. 19

Bullseye pattern halftoned with 150 lines/inch, 0 degree screen; printed at 600 dpi and scanned at 600 dpi. The red dots indicate detected MIF’s

Printer MIF Detection Result

Maximal frequency: 90 cycles/inch Maximal frequency: 55 cycles/inch


Slide No. 20

MIF Detection on Test PageRadial Frequency

(cycles per inch)

Angle

(degrees)

37 ±90

50 ±90

75 ±90

57 ±64

67 ±64

75 ±64

50 ±45

72 ±45

75 ±45

57 ±26

67 ±26

75 ±26

37 0

45 0

75 0


Slide No. 21

MIF detection in the continuous-tone input image

Based on press profile, measure the energy of MIF in power

spectrum of the digital image

Find peaks in the spectrum of the continuous-tone image that

corresponding to MIF frequency

In frequency domain, calculate a confidence measure in the

neighborhood of the peaks

Calculate the size of each detected region to eliminate false

alarms due to strong edge components


Slide No. 22

MIF Detection on Digital Images

Sampling frequency of the digital image on print-out:

Image Metadata in PPML or XML Dimension: image width/height size

Position: Determined by the attribute “Position” in MARK and OBJECT elements

Transform Matrix: provides various image properties such as scale, skew, and translation

Clipping size: determined by the attribute “Rectangle” in CLIP_RECT element

S w

W, w : digital image size in pixels, W : image print out size in inches


Slide No. 23

Indices Representing MIF in Frequency Domain

Check for MIF on the 2D-DSFT of the digital image:

X[k,l] x[m,n]e j 2 kn / Ne j 2 lm / N

n0

N 1

m0

N 1

k̂ [F cos( )N / S]R

l̂ [F sin( )N / S]R

[g]R : Round off to the nearest integer

F is MIF amplitude and is the angle of MIF


Slide No. 24

Confidence Measurement in Frequency Domain

In frequency domain, calculate

a confidence measure in the

neighborhood of the peaks

N N pixel region centered at pixle x[ %m, %n] :

h%m,%n {x[m,n] : m %m N / 2, n %n N / 2},

2D power specturm of the region:

P%k ,%l%m,%n H %m,%nH *

%m,%n , where H %m,%n 2D-DSFT {h%m,%n},

Confident measure:

C%k ,%l%m,%n Pmax

center / Pmaxflat ,

M [ %m, %n] 1, C%k ,%l

%m,%n Cth and P%k ,%l%m,%n VTth ,

0, else.

Power spectrum

k,%l

Pmaxcenter : maximal of the power spectrum of

Pmaxflat : maximal of the power spectrum of


Slide No. 25

Confidence Measure

Strong peak in power

spectrum at the MIF location

means perceptible moiré is

likely to occur in printing

Confidence measure helps to

reduce misclassification

Power Spectrum


Slide No. 26

Results: Sinusoidal Grating

Digitally generated sinusoidal grating

Starting from 10 cycles/inch with 20

cycles/inch increment per row

Starting from 0 degree with 10 degrees

increment per column

Detection is done for 90 cycles/inch

with 10 degrees


Slide No. 27

Misclassification Due to Strong Edges


Slide No. 28

Measure Length and Width of Each Detected Region

Project each region to the

horizontal and vertical axis of

the image plan

Count the number of pixels

on each horizontal and

vertical position

Regions with maximal length

or width less than 2N (N: the

2D DSFT window size) will

be removed from mask.

Projection to obtain width

region identified in moiré mask


Slide No. 29

Misclassification Regions Removed


Slide No. 30

Adaptive Scaling to Reduce Moiré

For each image identified with moiré we scaled the image to reduce

moiré artifacts in print-out

Each region on the moiré mask is analyzed to obtain a scale factor

Global scale factor is the maximal of all the regional scale factors

Entire image is scaled by the global factor


Slide No. 31

Results: Shirt

Printed using HP LaserJet

5500 with 600 dpi and 150 lpi

halftone

Visible moiré artifacts on the

shirt region

Successful detection of using

the printer profile


Slide No. 32

Results: Hotel

Original digital image

Moiré mask

Scan of the original image print-out

Scan of the scaled image print-out


Slide No. 33

Results: Kodak WindowOriginal digital image

Moiré mask

Scan of the original image print-out

Scan of the scaled image print-out


Slide No. 34

Summary

Analyze the relationship between the spectrum of halftone

image and that of the original image

Use bullseye pattern to characterize printer

Identified moiré inducing frequency

Predict moiré artifacts based on the image content, image

pixel size, and actual printed size

Adaptive image scaling to resize the image so that the new

image will not induce moiré artifacts

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