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Purdue University 1

Print Quality Issues Related to Digital Print Quality Issues Related to Digital Printing and Forensic ApplicationsPrinting and Forensic Applications

Osman Arslan†

Gazi N. Ali†Professor George T. Chiu‡

Professor Edward J. Delp†

Professor Jan P. Allebach†

†School of Electrical and Computer Engineering‡School of Mechanical Engineering

Purdue University,West Lafayette, Indiana

Purdue University 2

IntroductionIntroduction

• Research activities in Purdue university

• Imaging pipeline

• EP and inkjet printing basics

• Application examples

Purdue University 3

Digital Print Systems (DPS)Digital Print Systems (DPS)program at Purdueprogram at Purdue

• Started in 1986 by Jan Allebach with funding from Mead Imaging

• Focus on imaging systems rather than image processing per se

• Major growth in 1992 with funding by HP and Kodak and participation by Charles Bouman

• Today the DPS program supports approximately 30 half-time graduate research assistants and 8 faculty members in five different academic units at Purdue

Purdue University 4

Need for multidisciplinary approachesNeed for multidisciplinary approaches

DocumentFile

Imaging pipeline

Media (paper) and colorants

Printer mechanism

Human viewer

Purdue University 5

Interdisciplinary natureInterdisciplinary natureof the researchof the research

• ECEJan AllebachCharlie BoumanEd DelpSam Midkiff18 students

• IEMark LehtoYuehwern Yih3 students

• MEGeorge Chiu

5 students

• PsychologyZygmunt Pizlo

• Summary4 departments

8 faculty members

26 students

34 researchers

Purdue University 6

Who is sponsoring the research?Who is sponsoring the research?

• Curent sponsorsHP

Samsung

Xerox

National Science Foundation with guidance from U.S. Secret Service

DuPont

• Previous sponsorsApple Computer

Color Savvy Systems

Eastman Kodak

LG Electronics

Mead Imaging

Purdue University 7

Imaging pipeline is complexImaging pipeline is complex

Purdue University 8

Printing Technology

Non Impact Impact

Laser

Inkjet

Solid Ink

Dye Sublim

ation

Therm

al Wax

Therm

al Autochrom

e

Dot M

atrix

Character

Printing TechnologyPrinting Technology

Purdue University 9

OPCDrum

Diode Laser

DeveloperRoller

TonerSupply

TransferRoller

Charge Roller

Cleaning

Fuser

RotatingPolygonMirror

PAPE

R

ProcessDirection

ScanDirection

Electrophotographic (laser) printing processElectrophotographic (laser) printing process

Purdue University 10

Six Steps of Six Steps of ElectrophotographyElectrophotography


Inkjet Printer Mechanism Inkjet Printer Mechanism

Bubblejet/ Thermal Piezoelectric


Commercial presses are based on “impact” Commercial presses are based on “impact” printing technologies printing technologies

• Letterpress and flexography

• Offset lithography

• Gravure

• Intaglio

Heidelberg Speedmaster SM 74 offset press 20”x29”, 2-color, 10K sheets/hr.


Digital Digital halftoninghalftoning::rendering gray levelsrendering gray levels

• The perception of levels of gray intermediate to black or white depends on a local average of the binary texture.


Digital Digital halftoninghalftoning::rendering detailrendering detail

• Detail is rendered by local modulation of this texture.


HalftoningHalftoning algorithmsalgorithms• Point processes - screening

• Neighborhood processes - error diffusion

• Iterative processes - direct binary search (DBS)

DBS screen Error diffusion

Di

DBS

Increasing complexity

Increasing quality


Impact of the research: use in products and Impact of the research: use in products and media coveragemedia coverage

• Resolution synthesis algorithm in the drivers for 10’s of millions of units of inkjet printers

• Tone-dependent error diffusion in the hardware for 10’s of millions of units of inkjet printers

• AM/FM halftoning in firmware for midrange laser MFP products

• Print quality defect diagnostics website on-line for midrange color laser products

• Printer forensics research reported in over 24 media outlets, including the BBC, The Economist, EE Times, and Forbes (see http://shay.ecn.purdue.edu/~prints for complete set of articles)

http://shay.ecn.purdue.edu/~prints


Resolution synthesis yields sharper images Resolution synthesis yields sharper images (4X scaling results) for inkjet products(4X scaling results) for inkjet products

Tree-Based Resolution SynthesisPhotoshop Bicubic Interpolation


ToneTone--dependent error diffusion improvesdependent error diffusion improveshalftone quality for inkjet productshalftone quality for inkjet products

Floyd-Steinberg TDED


AMFM AMFM halftoninghalftoning suppresses moire in scansuppresses moire in scan--toto--print print applications for laser MFP productsapplications for laser MFP products

AM/FM halftoning

Floyd-Steinbergerror diffusion

PhotoTone

120 line frequency bar 160 line frequency bar


The hybrid screen provides superiorThe hybrid screen provides superiorquality at lowquality at low--bit depthsbit depths

130x130, 34-degree screen (a: absorptance level)

a = 1/52 a = 1/13 a = 2/13 a = 3/13 a = 4/13

Dispersed dots Periodic clustered dots


Laser printer test pages provide advancedLaser printer test pages provide advancedfeatures for diagnosis of print quality defectsfeatures for diagnosis of print quality defects

CPR test block

Divided sections

Ghosting test bar

Constant tone background

Rulers

Page number


Printer Defects and Objective Metrics for Printer Defects and Objective Metrics for Print QualityPrint Quality


OutlineOutline

• Print quality defects

• One of the most serious print defects: Banding

• Print quality test page

• Objective metrics for print qualityMethod of computing objective metrics

Line metrics: An example


Print Quality Defects Print Quality Defects

• Defects are often introduced into the images because of mechanical or material problems during imaging

• The defects may be introduced due toRendering technique and mechanical design of the printing device

Equipment failure


Classification of Print Quality DefectsClassification of Print Quality Defects

• Group 1: Defects of uniformityBanding, streaks, second side discharge marks

• Group 2: Random marks and repetitive artifactRandomly scattered white specks, repetitive marks, repetitive lines, ghosting, leaked toner, tone bubbles, tone scatter

• Group 3: Color defectsColor plane registration, color consistency


Print Quality DefectsPrint Quality Defects

• Defects of uniformity

Streaks

Paper process direction

Banding


Print Quality DefectsPrint Quality Defects• Random marks or repetitive artifacts

Randomly scattered white

specks

Repetitive marks

Ghosting


Illustration of BandingIllustration of Banding

}banding


Origins of BandingOrigins of Banding• An artifact affecting image macro/micro uniformity

Periodic or random – periodic is most objectionable

Gear transmission error is one of the major contributors» Eccentricity and tooth profile error cause scan line spacing variation

0 50 100 150 200 250 3000

2000

4000

6000

8000

10000

frequency (cycles/rev)

sign

al p

ower

(|H

|2 )

193

324 5

m1

235 4

6

78

m

1

24

Spectrum


Banding Frequency Determination

• Vertical line patterns eliminate the effect of halftone

• Vary the line spacing to control gray level

1-D horizontal projection

(printed and scanned page)


Sample Banding Spectra

Minolta 1250 Brother 1440

cycles/incycles/inab

sorp

tanc

e

abso

rpta

nce


Spectra of projected absorptance for LJ 1000 Spectra of projected absorptance for LJ 1200

Spectra of projected absorptance for LJ 4050 Spectra of projected absorptance for ML-1450

Sample Banding Spectra


Banding Frequencies for Banding Frequencies for Various EP PrintersVarious EP Printers

Printer Model Banding Frequencies (cycles/inch)

Minolta LaserJet 1250 17

Brother LaserJet 1440 30, 73, 78

HP LaserJet 1000 27, 69

HP LaserJet 1200 69

HP LaserJet 4050 51, 100

Samsung ML-1450 16, 32, 100, 106


Print Quality Test PagePrint Quality Test Page

CPR test block

Divided sections

Ghosting test bar

Constant tone background

Rulers

Page number


Feature : Ghosting Test BlockFeature : Ghosting Test Block

• Dark test bar generates visible ghosting on light background

(b)

GhostingStructured

Test bar

Background

Structured test barDistinct from a vertical line defectMeasure of ghosting strength

(a)

GhostingTest bar

BackgroundPaper

process direction


Feature : RulerFeature : Ruler

• Information provided by rulersDistance information

Location information

• Label differentiationHorizontal: numbers

Vertical: alphabetical characters

Ghosting on the test page containing rulers

Ghosting image

Distance informationfor ghosting

Test bars


Print Quality MetricPrint Quality Metric

• We have to define the attributes that will tell us about print quality

• We also need to come up with objective quantitative metrics to evaluate these attributes

• ISO/IEC has already provided guidelines on hardcopy print quality assessment


Objective Metrics for Print QualityObjective Metrics for Print Quality

Line MetricsSolid-Fill Metrics

Background Field Metrics

Tint Solid Metrics

Blurriness

Stroke width

Raggedness

Contrast

Fill

Darkness

Extraneous marks

Background haze

Overall darkness

Mottle

Large area density variation (LADV)

Voids

Overall darkness

Large area density variation (LADV)

Mottle

Granularity

Extraneous marks

Background uniformity


ISO/IEC Metric DefinitionISO/IEC Metric Definition


Test Target (File) Printer Printed Target

Scanner Workstation/PC Metric Values

Spot Sold Area

Darkness= 0.6051

Mottle= 0.0134

LADV = 0.084

Method of Computing Objective MetricsMethod of Computing Objective Metrics


Output Variation Due to Technology and MediaOutput Variation Due to Technology and Media

Laser Printer Using Coated Paper Laser Printer Using Cotton Bond


Output Variation Due to Technology and MediaOutput Variation Due to Technology and Media

Ink-jet printer using standard paper Ink-jet printer using special ink-jet paper


Line Metrics: An ExampleLine Metrics: An Example


Intrinsic and Extrinsic Features for Printer Intrinsic and Extrinsic Features for Printer IdentificationIdentification


OutlineOutline

• Intrinsic and extrinsic features

• Principal component analysis for feature extraction

• Gaussian mixture model for classification

• Laser exposure modulation for embedding extrinsic signature


Intrinsic and Extrinsic FeaturesIntrinsic and Extrinsic Features

• Use intrinsic signature of printer to identify as much information as possible from printed document about printer that produced it

• Embed auxiliary information in document at time of printing via extrinsic signature

• Intrinsic and extrinsic signatures are based on extraction and modulation of physical characteristics of printer mechanism


Intrinsic Signature AnalysisIntrinsic Signature Analysis• Most signature features are stable from page to page and across

different printer cartridges

• Some signature features do vary from page to page, and may depend on the cartridge too

• Measurements need to be made over large number of samples to show a robust signature

• Need to develop database for all possible intrinsic signature patterns


Test Bed for Printer AnalysisTest Bed for Printer Analysis• 20 different printer models

5 inkjet, 2 multifunction and 13 electrophotographic (laser and LED)8 different manufacturersAt least 2 of each model

• 5 image capture systemsSaphir Ultra2 (1200 dpi)HP Scanjet 4570C (2400 dpi)HP Scanjet 8250 (4800 dpi)AZTEK Premier (8000 dpi)QEA IAS 1000 system


Intrinsic Signature Intrinsic Signature ––Fine Pitch BandingFine Pitch Banding

• Caused by quasiperiodic fluctuations in speed of rotating components

• For EP (laser or LED) printers, fluctuations in speed of rotation of optical photo-conductor is major source

• This artifact appears as cyclic light and dark bands perpendicular to the print process direction with relatively short period

• Effect is most prominent in midtone regions


Principal Components Analysis (PCA)Principal Components Analysis (PCA)

• Classical PCA is a linear transform that maps the data into a lower dimensional space by preserving as much data variance as possible

• Principal components are the features that can be used by the classifier


Dimension Reduction by PCADimension Reduction by PCA• Projection data is high dimensional. Dimension is reduced by PCA• All experimental data to be reported today is based on scans of the character "I"• For each printer, we obtain 40-100 projections from different repetitions of the

character "I"• Each projection is mean subtracted and normalized • For PCA, singular value decomposition is used

PCA


• The class separation is NOT suitable for classification

• PCA needs to be modified for better class separation

PCA for Five Printer ModelsPCA for Five Printer Models


Modified PCAModified PCA

• The eigenvectors can be determined using the within class scatter matrix and between class scatter matrix

• The generalized eigenvectors of SB and SW maximize the ratio of between-class scatter and to the within-class scatter

• SW is generally not invertible for real data

NT

c cn c c cn n=1c

Cc

W cc=1

B W

1Covariance matrix of a class c, Σ = t (x-μ )(x-μ ) , t = class labelN

NWithin-class scatter matrix, S = ΣN

Between-class scatter matrix, S =Σ-S , Σ = covariance matrix of the data

∑

∑

− =1W BS S φ λφ

•


Improvement Using Modified PCAImprovement Using Modified PCA

Original PCA Modified PCA


Gaussian Mixture Model (GMM)Gaussian Mixture Model (GMM)

• PCA gives the features but PCA is not a classifier

• Classification can be done by using Gaussian mixture model


GMM Parameter EstimationGMM Parameter Estimation

• A model with M component is,

• The component density function is,

• Initialization by K-means algorithm, 7 iterations. Training by EM algorithm, 25 iterations

1( ) ( ) ( | ), where P(j) are the mixing coefficients,

M = number of different printer models

M

jp z P j p z j

=

= ∑

2

22 2

1( | ) exp2(2 )

j

djj

z μp z j

σπσ

⎧ ⎫−⎪ ⎪= ⎨ ⎬⎪ ⎪⎩ ⎭


Unknown Printer Identification Using PCA and Unknown Printer Identification Using PCA and GMMGMM

LJ4050 LJ1200 LJ1000 14e ML1450 Majority

Vote

LJ4050 40 0 0 0 0 LJ4050

LJ1200

LJ1000

14e

ML1450

LJ1200 0 25 15 0 0

LJ1000 0 35 5 0 0

14e 0 0 0 40 0

ML1450 0 0 0 0 40

Test

Prin

ter

Classifier Output

Correctly Classified Incorrectly Classified


Embedding Extrinsic Signature Embedding Extrinsic Signature

• Modulating laser exposure to generate banding signals

• These banding frequencies should be different from the intrinsicfeatures of the printers

• Modulation should keep below human visual contrast sensitivity threshold but still be detectable from the scanner


Laser Exposure ModulationLaser Exposure Modulation

Laser Exposure PrintoutReferenceVoltage

Dot Size(Contrast)

Periodic Signal

OPC VoltageLaser Intensity

Voltage


Synchronizing the Exposure Modulation Synchronizing the Exposure Modulation with Scan Linewith Scan Line

• Extrinsic signature exposure modulation changes from scan line to scan line

• Require synchronizing the laser exposure modulation and beam detect signal


Test Target for Dot Size MeasurementTest Target for Dot Size Measurement

FFFF000000000000000000000000

00000000FFFF0000000000000000

0000000000000000FFFF00000000

000000000000000000000000FFFF

Print out

1.1V1.3V1.5V1.7V

4321

Scan line

1.1V1.3V1.5V1.7V

Hardware ready bit Reference voltage

4321

Scan line


Analysis of Dot Size ModulationAnalysis of Dot Size Modulation

• Modulation result for single dot Developed dot sizes are measured based on 8000 dpi scan

Dot size number of pixels with absorptance > 0.1

Average the dot size of 16 dots in a single line

1.1V 1.3V 1.5V 1.7V

Developeddot profile

Laser spot profile

Reference voltage

1 1.2 1.4 1.6 1.8 20

200

400

600

800

1000

1200

1400dot size stochastic

modulation voltage (volt)

Dot

siz

e (n

umbe

r of p

ixel

)


Embedding and Detecting an Extrinsic Embedding and Detecting an Extrinsic SignatureSignature

Process direction

Projection

DFT

1.1V1.3V1.5V1.7V

Periodic signal

Reference voltage Print out


Experimental ResultExperimental Result• Without modulation • With modulation

0 100 200 3000

1

2

3

4

5

cycle/in

FF

T 100

120 150

0 100 200 3000

1

2

3

4

5

cycle/in

FF

T

Modulation freq. with same

halftone frequency

Intrinsic banding

VΔ


Printer Identification from Printed Printer Identification from Printed Documents Using Texture Based Features Documents Using Texture Based Features


OutlineOutline

• Identification of EP Printers Process for printer identification

Texture features » Gray-Level Co-occurrence Matrix (GLCM)» Pixel based

Classification method

Classification example

Feature refinement

• Identification of Inkjet PrintersOverview of Inkjet Printers

Process for Printer Identification



Process for Printer IdentificationProcess for Printer Identification


Test CharacterTest Character

• Test classifier using letter “e”, because it is the most common letter used in English.

12pt. ‘e’ (Times Roman)


Selection Criteria for the FeaturesSelection Criteria for the Features

• Features should be robust to certain variations in the printers.

Feature : Average gray level of a character – may heavily depend on the amount of colorant left in the cartridge.

• Features should not depend directly on the size or type of font of the character.

Feature : Length/width of a character – will directly depend on the type and size of the font used in that document.


GrayGray--Level CoLevel Co--occurrence Matrix occurrence Matrix (GLCM) to Calculate Texture Features(GLCM) to Calculate Texture Features

• First proposed by Robert M. Haralick et. al. in 1973†

• Each entry pglcm(n,m) of the GLCM gives the frequency of occurrence of pairwisegraylevels, n and m, d pixels apart at an angle α

Img(i,j)i

j

† Robert M. Haralick, K. Shanmugam and Its’Hak Dinstein, Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 610 (1973)

α = 135o 2d =


An Example of a GrayAn Example of a Gray--Level Level CoCo--occurrence Matrixoccurrence Matrix

1 0 2 3 1 21 2 3 2 1 12 3 2 0 1 23 2 1 0 2 22 1 1 2 3 20 2 2 3 2 1

P(i,j,d,45o) with will be 2d =

i \ j 0 1 2 30123

0 3 0 03 2 1 00 2 9 00 0 1 4


Selection of (GLCM) ParametersSelection of (GLCM) Parameters

• Assume banding signal is primary source of texture in printed areas of document

• Choose α such that pixel pairs are chosen in the process direction (direction of banding signal)

• Vary distance, d between 1 to 10 and find the distance that performs the best separation between the classes.


Feature SetFeature SetVariance of pixels in ROI Correlation of entries in pglcm

Entropy of pixels in ROI

Mean of marginal probability densities of GLCM

Variance of marginal probability densities of GLCM

Energy of pglcm

Entropy measures of pglcm

Maximum entry in pglcm

Diagonal correlation

Energy of D(k) (Difference Histogram)

Entropy of D(k)

Inertia of D(k)

Local homogeneity of D(k)

Energy of S(k) (Sum Histogram)

Entropy of S(k)

Variance of S(k)

Cluster Shade of S(k)

Cluster prominence of S(k)

2Imgσ

Imgh

rμ

cμ2rσ2cσ

Energy

1hxy2hxy

glcmh

MaxProb

nmρdiagcorr

Denergy

Dh

DI

DLSenergy

Sh2Sσ

DA

DB


Printers Used in ExperimentPrinters Used in Experiment

Make Model DPIBrother hl1440 1200HP lj4050 600Lexmark e320 1200HP lj1000 600HP lj1200 600HP lj5M 600HP lj6MP 600Minolta 1250W 1200Okidata 14e 600Samsung ml1430 600


Classification Results: d = 5Classification Results: d = 5all features, 300 test vectorsall features, 300 test vectors

hl1440 lj4050 e320 lj1000 lj1200 lj5M lj6MP 1250W 14e ml1430 Majority Votehl1440 197 0 1 1 0 11 6 57 21 6 hl1440lj4050 0 300 0 0 0 0 0 0 0 0 lj4050e320 0 0 248 0 2 0 0 36 13 1 e320lj1000 4 0 0 152 66 5 11 7 4 51 lj1000lj1200 3 0 0 99 130 14 11 13 1 29 lj1200lj5M 60 0 1 1 7 165 29 30 5 2 lj5Mlj6MP 30 0 14 11 6 28 153 29 9 20 lj6MP1250W 33 0 49 2 1 7 4 181 20 3 1250W14e 74 0 25 1 2 2 3 128 62 3 1250Wml1430 10 0 9 61 15 21 30 13 17 124 ml1430

Correctly ClassifiedIncorrectly Classified

Bold = 2nd highest classification

Classifier Output

Test

Prin

ter


Feature RefinementFeature Refinement

• Repeat classification with 4 manually chosen features that yielded good discrimination based on observation

(1) Variance of ROI pixel values

(2) Entropy of ROI pixel values

(3) Mean of marginal row probability of GLCM

(7) Energy of GLCM


Feature Scatter PlotFeature Scatter Plot


Classification Results: d=9Classification Results: d=94 features, 300 test vectors4 features, 300 test vectors

hl1440 lj4050 e320 lj1000 lj1200 lj5M lj6MP 1250W 14e ml1430 Majority Votehl1440 142 0 0 3 2 26 12 67 41 7 hl1440lj4050 0 300 0 0 0 0 0 0 0 0 lj4050e320 0 0 283 0 0 1 0 12 4 0 e320lj1000 7 0 0 151 80 24 27 8 0 3 lj1000lj1200 12 0 1 140 91 28 21 4 0 3 lj1000lj5M 51 0 1 6 8 188 22 24 0 0 lj5Mlj6MP 32 0 25 51 45 40 65 17 0 25 lj6MP1250W 37 0 101 0 1 32 11 115 3 0 1250W14e 97 0 30 1 0 0 1 38 117 16 14eml1430 42 0 1 15 15 1 39 9 45 133 ml1430

Correctly ClassifiedIncorrectly Classified

Bold = 2nd highest classification

Test

Prin

ter

Classifier Output


OutlineOutline• Identification of EP Printers

Process for printer identification

Texture features » Gray-Level Co-occurrence Matrix (GLCM)» Pixel based

Classification method


Feature refinement

• Identification of Inkjet PrintersOverview of Inkjet Printers

Process for Printer Identification



Inkjet print mechanismInkjet print mechanism

Photos courtesy Hewlett-Packard Co.


PrintheadPrinthead nozzle geometrynozzle geometry

Nozzlecolumns

NozzlecolumnsDrop trajectory

Ink feed slotSilicon Silicon

magentacyan yellow

Nozzle plate

Intended target

Front view

Top view


Inkjet Printer ArtifactsInkjet Printer Artifacts

• IJ printers do render dots having a nearly hard, ideal profile, and much more stable than those rendered by EP printers

• However, there exist artifacts, which are unique to or more significant in inkjet printing process

Ink coalescence (firing adjacent nozzles simultaneously)

Satellites (firing the nozzle at higher frequency than they can handle)

Random dot placement errors


Samples Inkjet Printer DotsSamples Inkjet Printer Dots

Satellite

Single Dot Double Dot

Double Dot with a tail

Tail


MultiMulti--pass Printing and Print Maskpass Printing and Print Mask

Pen SweepDirection

MediaAdvanceDirection

Vertical positionof pen for the 1st pass

Vertical positionof pen for the 2nd pass

1 0 1 00 1 0 11 0 1 00 1 0 10 1 0 11 0 1 00 1 0 11 0 1 0

1 0 1 00 1 0 11 0 1 00 1 0 10 1 0 11 0 1 00 1 0 11 0 1 0

• Multiple-pass printing & print mask prevent artifacts such as the ink coalescence and satellites, but NOT dot placement error.

• Inkjet printers have different modes to produce different image quality and speed.

Single-pass and multi-pass modes

Faster or slower print head speeds.


Test pattern printing and scanningTest pattern printing and scanning

even

odd

even

odd

Printout (scanned) + Segmentation map

* scanned@4000dpi

Test pattern (600x600)


Calculation of dotCalculation of dotdisplacement statisticsdisplacement statistics

ref. line

ref. line

* ref. line = averaged centroid* displacement = centroid - ref. line


Printer CharacteristicsPrinter CharacteristicsHorizontal dot displacements

for even rasterHorizontal dot displacements

for odd raster

Vertical dot displacementsfor even raster

Vertical dot displacements for odd raster


Process for Inkjet Printer IdentificationProcess for Inkjet Printer Identification

Printed document

Softcopy version of

the documentExtracted characters

Feature space

Test

stability of the feature within

a printer

model

ScanningDe-skewing, segmentation

Textural feature

calculation

Eliminate feature

Discriminantanalysis

Selected feature set for

classification

BAD

GOOD


Sample Test Characters Scanned at 2400 dpiSample Test Characters Scanned at 2400 dpi

Cannon S330(High)

Cannon S330(Standard) HP 3420

(Best)HP 3420(Normal)

Epson C62 (BestPhoto)

Epson C62(Text&Image)


Printers Installed in the Printer BankPrinters Installed in the Printer Bank

Make Model ModeHP 3420 NormalHP 3650 NormalHP 1315 NormalLexmark Z25 BetterLexmark Z2250 NormalCanon S330 Standard



0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6

2.7

2.8

2.9

3

3.1

3.2

3.3

3.4

Max. Correlation Coeff. (θ=90o, d=16)

Ent

roph

y (θ

=90

o , d=

2)HP 3420HP 3650HP psc1315Lexmark Z25Lexmark Z2250Canon S330



1 2 3 4 5 6 7 8 90.85

0.86

0.87

0.88

0.89

0.9

0.91

0.92

0.93

0.94

Contrast ( =90o, d=2)

Contrast (θ=90o, d=2)

Max

. Cor

rela

tion

Coe

ff. (θ

=90

o , d=

1)

HP 3420

HP 3650

HP psc1315

Lexmark Z25

Lexmark Z2250

Canon S330


Thanks for your attention.Thanks for your attention.

Osman Arslan [email protected]

Gazi Naser Ali [email protected]

George T.-C. Chiu [email protected]

Edward J. Delp [email protected]

Jan P. Allebach [email protected]


mailto:[email protected]







ReferencesReferences

• J. Grice and J. P. Allebach, “The Print Quality Toolkit: An Integrated Print-Quality Assessment Tool,” Journal of Imaging Science and Technology, Vol. 43, pp. 187-199, March/April 1999.

• D. Kacker, T. Camis, and J. P. Allebach, “Electrophotographic Process Embedded in Direct Binary Search,” IEEE Trans. on Image Processing, Vol. 11, pp. 234-257, March 2002.

• G. Y. Lin, J. M. Grice, J. P. Allebach, G. T. C. Chiu, W. Bradburn, and J. Weaver, “Banding Artifact Reduction in Electrophotographic Printers by Using Pulse Width Modulation,” Journal of Imaging Science and Technology, Vol. 46, pp. 326-337, July/August 2002.

• M. T. S. Ewe, J. M. Grice, G. T. C. Chiu, and J. P. Allebach, C. S. Chan, W. Foote, “Banding Artifact Reduction in Electrophotographic Processes Using a Piezoelectric Actuated Laser Beam Deflection Device,” Journal of Imaging Science and Technology, Vol. 46, pp. 433-442, September/October 2002.

• C-L. Chen, G. T. C. Chiu, and J. P. Allebach, “Banding Reduction in Electrophotographic Processes Using Human Contrast Sensitivity Function Shaped Photoreceptor Velocity Control,” Journal of Imaging Science and Technology, Vol. 47, pp. 209-223, May/June 2003.

• G. N. Ali, A. K. Mikkilineni, P. J. Chiang, J. P. Allebach, George T. Chiu, and E. J. Delp, “Intrinsic and Extrinsic Signatures for Information Hiding and Secure Printing with Electrophotographic Devices,”Proceedings of IS&T’s NIP 19: International Conference on Digital Printing Technologies, New Orleans, LA, 28 September – 3 October 2003.pp. 511-515.

• Y. Bang, Z. Pizlo, N. Burningham, and J. P. Allebach, “Discrimination Based Banding Assessment,”Proceedings of IS&T’s NIP 19: International Conference on Digital Printing Technologies, New Orleans, LA, 28 September – 3 October 2003.


ReferencesReferences• A. K. Mikkilineni, G. N. Ali, P. Chiang, G. T. C. Chiu, J. P. Allebach, and E. J. Delp, “Signature-Embedding in

Printed Documents for Security and Forensic Applications,” Security, Steganography, and Watermarking of Multimedia Contents IV, E. J. Delp and P. W. Wong, eds, SPIE Vol. 5306, San Jose, CA, 18-22 January 2004, pp. 455-466.

• W. Jang, M. C. Chen, J. P. Allebach, and G. T. C. Chiu, “Print Quality Test Page,” Journal of Imaging Science and Technology, Vol. 48, pp. 432-446, Sept./Oct. 2004.

• P. Chiang, G. N. Ali, A. K. Mikkilineni, G. T. C. Chiu, J. P. Allebach, and E. J. Delp, “Extrinsic Signatures Embedding Using Exposure Modulation for Information Hiding and Secure Printing in Electrophotographic Devices,”Proceedings of IS&T’s NIP 20: International Conference on Digital Printing Technologies (Invited paper), Salt Lake City, UT, 31 October – 5 November 2004.

• G. N. Ali, A. K. Mikkilineni, P. Chiang, J. P. Allebach, G. T. C. Chiu, and E. J. Delp, “Application of Principal Components Analysis and Gaussian Mixture Models to Printer Identification,” Proceedings of IS&T’s NIP 20: International Conference on Digital Printing Technologies (Invited paper), Salt Lake City, UT, 31 October – 5 November 2004.

• A. K. Mikkilineni, G. N. Ali, P. Chiang, G. T. C. Chiu, J. P. Allebach, and E. J. Delp, “Printer Identification Based on Textural Features,” Proceedings of IS&T’s NIP 20: International Conference on Digital Printing Technologies, Salt Lake City, UT, 31 October – 5 November 2004.

• O. Arslan, J. P. Allebach, and Z. Pizlo, “Softcopy Banding Visibility Assessment,” Image Quality and System Performance II, R. Rasmussen and Y. Miyake, eds, SPIE Vol. 5668, San Jose, CA, 16-20 January 2005.

• A. K. Mikkilineni, P. Chiang, G. N. Ali, G. T. C. Chiu, J. P. Allebach, and E. J. Delp, “Printer Identification Based on Graylevel Co-Occurrence Features for Security and Forensic Applications,” Security, Steganography, and Watermarking of Multimedia Contents VII, E. J. Delp and P. W. Wong, eds, SPIE Vol. 5681, San Jose, CA, 16-20 January 2005.

• W. Jang and J. P. Allebach, “Simulation of Print Quality Defects,” (Feature Article) Journal of Imaging Science and Technology, Vol. 49, pp. 1-18, Jan./Feb. 2005.

• J. H. Lee and J. P. Allebach, “Inkjet Printer Model-Based Halftoning,” IEEE Trans. on Image Processing, Vol. 14, pp. 674-689, May 2005.