PrintPrint QualityQuality IssuesIssues RelatedRelated toto DigitalDigital PrintingPrinting andand ForensicForensic ApplicationsApplications

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 1 IntroductionIntroduction

• Research activities in Purdue university • Imaging pipeline • EP and inkjet printing basics • Application examples

Purdue University 2 DigitalDigital PrintPrint SystemsSystems (DPS)(DPS) programprogram atat PurduePurdue

• 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 3 NeedNeed forfor multidisciplinarymultidisciplinary approachesapproaches

Imaging Media (paper) pipeline and colorants

Document Human File viewer

Printer mechanism

Purdue University 4 InterdisciplinaryInterdisciplinary naturenature ofof thethe researchresearch

• ECE • ME Š Jan Allebach Š George Chiu Š Charlie Bouman Š 5 students Š Ed Delp • Psychology Š Sam Midkiff Š Zygmunt Pizlo Š 18 students • Summary • IE Š 4 departments Š Mark Lehto Š 8 faculty members Š Yuehwern Yih Š 26 students Š 3 students Š 34 researchers

Purdue University 5 WhoWho isis sponsoringsponsoring thethe research?research?

• Curent sponsors • Previous sponsors Š HP Š Apple Computer Š Samsung Š Color Savvy Systems Š Xerox Š Eastman Kodak Š National Science Š LG Electronics Foundation with Š Mead Imaging guidance from U.S. Secret Service Š DuPont

Purdue University 6 ImagingImaging pipelinepipeline isis complexcomplex

Purdue University 7 PrintingPrinting TechnologyTechnology

Printing Technology

Non Impact Impact Thermal Autochrome Thermal Dye Sublimation Dot Matrix Thermal Wax Thermal Character Solid Ink Inkjet Laser

Purdue University 8 ElectrophotographicElectrophotographic (laser)(laser) printingprinting processprocess

Diode Laser Rotating Polygon Mirror

Charge Roller Developer Roller Cleaning Toner Supply

Scan C

Direction R OP

Fuser E um P Dr

A P

Transfer Roller Process n Directio Purdue University 9 SixSix StepsSteps ofof ElectrophotographyElectrophotography

Purdue University 10 InkjetInkjet PrinterPrinter MechanismMechanism

Bubblejet/ Thermal Piezoelectric

Purdue University 11 CommercialCommercial pressespresses areare basedbased onon “impact”“impact” printingprinting technologiestechnologies

• Letterpress and flexography • Offset lithography • Gravure • Intaglio

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

Purdue University 12 DigitalDigital halftoninghalftoning:: renderingrendering graygray levelslevels

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

Purdue University 13 DigitalDigital halftoninghalftoning:: renderingrendering detaildetail

• Detail is rendered by local modulation of this texture.

Purdue University 14 HalftoningHalftoning algorithmsalgorithms

• Point processes - screening • Neighborhood processes - error diffusion • Iterative processes - direct binary search (DBS)

Di

DBS screen Error diffusion DBS

Increasing complexity

Increasing quality

Purdue University 15 ImpactImpact ofof thethe research:research: useuse inin productsproducts andand mediamedia coveragecoverage

• 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)

Purdue University 16 ResolutionResolution ssyynthesisnthesis yyieldsields sharpersharper imaimaggeses (4X(4X scalingscaling results)results) forfor inkjetinkjet productsproducts

Photoshop Bicubic Interpolation Tree-Based Resolution Synthesis

Purdue University 17 ToneTone--dependentdependent errorerror diffusiondiffusion improvesimproves halftonehalftone qualityquality forfor inkjetinkjet productsproducts

Floyd-Steinberg TDED Purdue University 18 AMFMAMFM halftoninghalftoning suppressessuppresses moiremoire inin scanscan--toto--printprint applicationsapplications forfor laserlaser MFPMFP productsproducts 120 line frequency bar 160 line frequency bar

AM/FM halftoning

Floyd-Steinberg error diffusion

PhotoTone

Purdue University 19 TheThe hybridhybrid screenscreen providesprovides superiorsuperior qualityquality atat lowlow--bitbit depthsdepths

Dispersed dots Periodic clustered dots

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

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

Purdue University 20 LaserLaser printerprinter testtest pagespages provideprovide advancedadvanced featuresfeatures forfor diagnosisdiagnosis ofof printprint qualityquality defectsdefects

Rulers CPR test block

Ghosting test bar

Divided sections

Page number Constant tone background

Purdue University 21 PrinterPrinter DefectsDefects andand ObjectiveObjective MetricsMetrics forfor PrintPrint QualityQuality

Purdue University 22 OutlineOutline

• Print quality defects • One of the most serious print defects: Banding • Print quality test page • Objective metrics for print quality Š Method of computing objective metrics Š Line metrics: An example

Purdue University 23 PrintPrint QualityQuality DefectsDefects

• Defects are often introduced into the images because of mechanical or material problems during imaging • The defects may be introduced due to Š Rendering technique and mechanical design of the printing device Š Equipment failure

Purdue University 24 ClassificationClassification ofof PrintPrint QualityQuality DefectsDefects

• Group 1: Defects of uniformity Š Banding, streaks, second side discharge marks

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

• Group 3: Color defects Š Color plane registration, color consistency

Purdue University 25 PrintPrint QualityQuality DefectsDefects

• Defects of uniformity Paper process direction

Banding Streaks

Purdue University 26 PrintPrint QualityQuality DefectsDefects • Random marks or repetitive artifacts

Randomly Repetitive scattered white marks specks

Ghosting

Purdue University 27 IllustrationIllustration ofof BandingBanding

}banding

Purdue University 28 OriginsOrigins ofof BandingBanding • An artifact affecting image macro/micro uniformity Š Periodic or random – periodic is most objectionable Š Gear transmission error is one of the major contributors » Eccentricity and tooth profile error cause scan line spacing variation

Spectrum 8 7 10000 193

8000 ) 2 5 3 4 6000 24 2 6 signal (|H| power 4000 m 2000 1

0 m 0 50 100 150 200 250 300 4 frequency (cycles/rev) 1 3 5 2

Purdue University 29 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)

Purdue University 30 Sample Banding Spectra

Minolta 1250 Brother 1440 absorptance absorptance

cycles/in cycles/in

Purdue University 31 Sample Banding Spectra

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

Purdue University 32 BandingBanding FrequenciesFrequencies forfor VariousVarious EPEP PrintersPrinters

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

Purdue University 33 PrintPrint QualityQuality TestTest PagePage

Rulers CPR test block Ghosting test bar

Divided Page sections number Constant tone background

Purdue University 34 FeatureFeature :: GhostingGhosting TestTest BlockBlock

• Dark test bar generates visible ghosting on light background

z Structured test bar Š Distinct from a vertical line defect Š Measure of ghosting strength Structured Test bar Ghosting Test bar Ghosting

Background Background Paper process direction

(a) (b) Purdue University 35 FeatureFeature :: RulerRuler Distance information for ghosting

• Information provided by rulers Š Distance information Š Location information

Test Ghosting • Label differentiation bars image Š Horizontal: numbers Š Vertical: alphabetical characters

Ghosting on the test page containing rulers Purdue University 36 PrintPrint QualityQuality MetricMetric

• 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

Purdue University 37 ObjectiveObjective MetricsMetrics forfor PrintPrint QualityQuality

Solid-Fill Tint Solid Background Field Line Metrics Metrics Metrics Metrics

Blurriness Overall darkness Overall darkness Extraneous marks Stroke width Mottle Large area density Raggedness variation (LADV) Background Large area density uniformity Contrast variation (LADV) Mottle Fill Voids Granularity Darkness Extraneous marks Background haze

Purdue University 38 ISO/IECISO/IEC MetricMetric DefinitionDefinition

Purdue University 39 MethodMethod ofof ComputingComputing ObjectiveObjective MetricsMetrics

Test Target (File) Printer Printed Target

Spot Sold Area Darkness= 0.6051 Mottle= 0.0134 LADV = 0.084

Scanner Workstation/PC Metric Values

Purdue University 40 OutputOutput VariationVariation DueDue toto TechnologyTechnology andand MediaMedia

Laser Printer Using Coated Paper Laser Printer Using Cotton Bond

Purdue University 41 OutputOutput VariationVariation DueDue toto TechnologyTechnology andand MediaMedia

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

Purdue University 42 LineLine Metrics:Metrics: AnAn ExampleExample

Purdue University 43 LineLine Metrics:Metrics: AnAn ExampleExample

Purdue University 44 IntrinsicIntrinsic andand ExtrinsicExtrinsic FeaturesFeatures forfor PrinterPrinter IdentificationIdentification

Purdue University 45 OutlineOutline

• Intrinsic and extrinsic features • Principal component analysis for feature extraction • Gaussian mixture model for classification • Laser exposure modulation for embedding extrinsic signature

Purdue University 46 IntrinsicIntrinsic andand ExtrinsicExtrinsic FeaturesFeatures

• 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

Purdue University 47 IntrinsicIntrinsic SignatureSignature AnalysisAnalysis • 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

Purdue University 48 TestTest BedBed forfor PrinterPrinter AnalysisAnalysis

• 20 different printer models Š 5 inkjet, 2 multifunction and 13 electrophotographic (laser and LED) Š 8 different manufacturers Š At least 2 of each model

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

Purdue University 49 IntrinsicIntrinsic SignatureSignature –– FineFine PitchPitch BandingBanding

• 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

Purdue University 50 PrincipalPrincipal ComponentsComponents AnalysisAnalysis (PCA)(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

Purdue University 51 DimensionDimension ReductionReduction byby PCAPCA • 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

Purdue University 52 PCAPCA forfor FiveFive PrinterPrinter ModelsModels

• The class separation is NOT suitable for classification • PCA needs to be modified for better class separation

Purdue University 53 ModifiedModified PCAPCA

N • 1 T Covariance matrix of a class c, Σcc=t(x-∑ ncccμ )(x-μ ) , tn = class label Nc n=1 C Nc Within-class scatter matrix, SWc =∑ Σ c=1 N

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

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

−1 SSWBφλφ=

• 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

Purdue University 54 ImprovementImprovement UsingUsing ModifiedModified PCAPCA

Original PCA Modified PCA

Purdue University 55 GaussianGaussian MixtureMixture ModelModel (GMM)(GMM)

• PCA gives the features but PCA is not a classifier

• Classification can be done by using Gaussian mixture model

Purdue University 56 GMMGMM ParameterParameter EstimationEstimation

• A model with M component is,

M pz( )= ∑ P ( jpz ) ( | j ), where P(j) are the mixing coefficients, j =1 M = number of different printer models

• The component density function is,

2 ⎧ z − μ ⎫ 1 ⎪ j ⎪ pz(|) j = d exp⎨ 2 ⎬ 2 (2πσ ) 2 ⎪ 2σ j ⎪ j ⎩⎭

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

Purdue University 57 UnknownUnknown PrinterPrinter IdentificationIdentification UsingUsing PCAPCA andand GMMGMM Classifier Output

LJ4050 LJ1200 LJ1000 14e ML1450 Majority Vote LJ4050 40 0000 LJ4050

LJ1200 0 25 15 0 0 LJ1200

Test Printer LJ1000 0 35 5 00 LJ1000

14e 0 0 0 40 0 14e

ML1450 0 0 0 0 40 ML1450

Correctly Classified Incorrectly Classified Purdue University 58 EmbeddingEmbedding ExtrinsicExtrinsic SignatureSignature

• Modulating laser exposure to generate banding signals • These banding frequencies should be different from the intrinsic features of the printers • Modulation should keep below human visual contrast sensitivity threshold but still be detectable from the scanner

Purdue University 59 LaserLaser ExposureExposure ModulationModulation

Reference Laser Exposure Printout Dot Size Voltage (Contrast)

Laser Intensity OPC Voltage

Periodic Signal

Voltage

Purdue University 60 SynchronizingSynchronizing thethe ExposureExposure ModulationModulation withwith ScanScan LineLine

• Extrinsic signature exposure modulation changes from scan line to scan line • Require synchronizing the laser exposure modulation and beam detect signal

Purdue University 61 TestTest TargetTarget forfor DotDot SizeSize MeasurementMeasurement

Scan line Hardware ready bit Reference voltage

1 FFFF000000000000000000000000 1.7V 2 00000000FFFF0000000000000000 1.5V 3 0000000000000000FFFF00000000 1.3V 4 000000000000000000000000FFFF 1.1V

Scan line Print out 1 2 3 4 1.7V 1.5V 1.3V 1.1V

Purdue University 62 AnalysisAnalysis ofof DotDot SizeSize ModulationModulation

• Modulation result for single dot ¾ Developed dot sizes are measured based on 8000 dpi scan ¾ Dot size  number of pixels with absorptance > 0.1 ¾ Average the dot size of 16 dots in a single line

dot size stochastic Reference 1400 1.1V 1.3V 1.5V 1.7V voltage 1200

1000

Laser 800 spot profile 600

400 Dot size (number of pixel) Developed 200 dot profile 0 1 1.2 1.4 1.6 1.8 2 modulation voltage (volt)

Purdue University 63 EmbeddingEmbedding andand DetectingDetecting anan ExtrinsicExtrinsic SignatureSignature

Periodic Reference signal Projection voltage Print out 1.1V 1.3V 1.5V DFT 1.7V

Process direction

Purdue University 64 ExperimentalExperimental ResultResult • Without modulation • With modulation

5 5 halftone Modulation freq. 4 4 frequency with sameΔV 3 3 Intrinsic FFT FFT 2 banding 2 100

1 120 1 150

0 0 0 100 200 300 0 100 200 300 cycle/in cycle/in

Purdue University 65 PrinterPrinter IdentificationIdentification fromfrom PrintedPrinted DocumentsDocuments UsingUsing TextureTexture BasedBased FeaturesFeatures

Purdue University 66 OutlineOutline

• Identification of EP Printers Š Process for printer identification Š Texture features » Gray-Level Co-occurrence Matrix (GLCM) »Pixel based Š Classification method Š Classification example Š Feature refinement • Identification of Inkjet Printers Š Overview of Inkjet Printers Š Process for Printer Identification Š Classification example

Purdue University 67 ProcessProcess forfor PrinterPrinter IdentificationIdentification

Purdue University 68 TestTest CharacterCharacter

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

12pt. ‘e’ (Times Roman)

Purdue University 69 SelectionSelection CriteriaCriteria forfor thethe FeaturesFeatures

• Features should be robust to certain variations in the printers. Š 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. Š Feature : Length/width of a character – will directly depend on the type and size of the font used in that document.

Purdue University 70 GrayGray--LevelLevel CoCo--occurrenceoccurrence MatrixMatrix (GLCM)(GLCM) toto CalculateCalculate TextureTexture FeaturesFeatures

j • First proposed by Robert M. Haralick et. al. Img(i,j) in 1973† i

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

α = 135o d = 2

† 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)

Purdue University 71 AnAn ExampleExample ofof aa GrayGray--LevelLevel CoCo--occurrenceoccurrence MatrixMatrix

1 0 2 3 1 2 1 2 3 2 1 1 i \ j 0 1 2 3 0 0 3 0 0 2 3 2 0 1 2 o P(i,j,d,45 ) with d = 2 will be 1 3 2 1 0 3 2 1 0 2 2 2 0 2 9 0 2 1 1 2 3 2 3 0 0 1 4 0 2 2 3 2 1

Purdue University 72 SelectionSelection ofof (GLCM)(GLCM) ParametersParameters

• 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.

Purdue University 73 FeatureFeature SetSet

2 σ Img Variance of pixels in ROI ρ nm Correlation of entries in pglcm

diagcorr hImg Entropy of pixels in ROI Diagonal correlation Denergy μr Mean of marginal probability Energy of D(k) (Difference Histogram) densities of GLCM μc hD Entropy of D(k) 2 σ Variance of marginal Inertia of D(k) r I D 2 probability densities of GLCM σ c Local homogeneity of D(k) LD

Energy Energy of pglcm Senergy Energy of S(k) (Sum Histogram)

hxy1 hS Entropy of S(k) Entropy measures of p 2 hxy2 glcm σ S Variance of S(k)

hglcm AD Cluster Shade of S(k) MaxProb Maximum entry in pglcm BD Cluster prominence of S(k)

Purdue University 74 PrintersPrinters UsedUsed inin ExperimentExperiment

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

Purdue University 75 ClassificationClassification Results:Results: dd == 55 allall features,features, 300300 testtest vectorsvectors

Classifier Output hl1440 lj4050 e320 lj1000 lj1200 lj5M lj6MP 1250W 14e ml1430 Majority Vote hl1440 197 0110116 57 21 6 hl1440 lj4050 0 300 00000000 lj4050 e320 0 0 248 0200 36 13 1 e320 lj1000 4 0 0 152 66 5117451lj1000 lj1200 3 0 0 99 130 14 11 13 1 29 lj1200 lj5M 60 0117 165 29 30 5 2 lj5M

Test Printer lj6MP 30 01411628 153 29 9 20 lj6MP 1250W 33 0 49 2174 181 20 3 1250W 14e 74 0251223 128 62 3 1250W ml1430 10 0 9 61 15 21 30 13 17 124 ml1430

Correctly Classified Incorrectly Classified Bold = 2nd highest classification

Purdue University 76 FeatureFeature RefinementRefinement

• Repeat classification with 4 manually chosen features that yielded good discrimination based on observation Š (1) Variance of ROI pixel values Š (2) Entropy of ROI pixel values Š (3) Mean of marginal row probability of GLCM Š (7) Energy of GLCM

Purdue University 77 FeatureFeature ScatterScatter PlotPlot

Purdue University 78 FeatureFeature ScatterScatter PlotPlot

Purdue University 79 ClassificationClassification Results:Results: d=9d=9 44 features,features, 300300 testtest vectorsvectors

Classifier Output hl1440 lj4050 e320 lj1000 lj1200 lj5M lj6MP 1250W 14e ml1430 Majority Vote hl1440 142 00322612 67 41 7 hl1440 lj4050 0 300 00000000 lj4050 e320 0 0 283 0010 12 40e320 lj1000 7 0 0 151 80 2427803lj1000 lj1200 12 0 1 140 91 2821403lj1000 lj5M 51 0168 188 222400lj5M lj6MP 32 0 25 51 45 40 65 17 0 25 lj6MP Test Printer 1250W 37 0 101 013211 115 301250W 14e 97 030100138 117 16 14e ml1430 42 0 1 15 15 1 39 9 45 133 ml1430

Correctly Classified Incorrectly Classified Bold = 2nd highest classification

Purdue University 80 OutlineOutline

• Identification of EP Printers Š Process for printer identification Š Texture features » Gray-Level Co-occurrence Matrix (GLCM) » Pixel based Š Classification method Š Classification example Š Feature refinement • Identification of Inkjet Printers Š Overview of Inkjet Printers Š Process for Printer Identification Š Classification example

Purdue University 81 InkjetInkjet printprint mechanismmechanism

Photos courtesy Hewlett-Packard Co.

Purdue University 82 PrintheadPrinthead nozzlenozzle geometrygeometry

cyan magenta yellow

Front view

Intended target Nozzle Nozzle columns Drop trajectory columns Top view Nozzle plate Silicon Ink feed slot Silicon

Purdue University 83 InkjetInkjet PrinterPrinter ArtifactsArtifacts

• 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 Š Ink coalescence (firing adjacent nozzles simultaneously) Š Satellites (firing the nozzle at higher frequency than they can handle) Š Random dot placement errors

Purdue University 84 SamplesSamples InkjetInkjet PrinterPrinter DotsDots Single Dot Double Dot

Double Dot with a tail

Satellite

Tail

Purdue University 85 MultiMulti--passpass PrintingPrinting andand PrintPrint MaskMask

• Inkjet printers have different modes to produce different image quality and speed. Š Single-pass and multi-pass modes Š Faster or slower print head speeds.

1 0 1 0 Vertical position Pen Sweep st 0 1 0 1 of pen for the 1 pass 1 0 1 0 Direction 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 Media 0 1 0 1 Advance 1 0 1 0 Direction Vertical position of pen for the 2nd pass

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

Purdue University 86 TestTest patternpattern printingprinting andand scanningscanning

Printout (scanned) + Test pattern (600x600) Segmentation map even odd even odd

* scanned@4000dpi

Purdue University 87 CalculationCalculation ofof dotdot displacementdisplacement statisticsstatistics

ref. line

ref. line

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

Purdue University 88 PrinterPrinter CharacteristicsCharacteristics Horizontal dot displacements Horizontal dot displacements for even raster for odd raster

Vertical dot displacements Vertical dot displacements for even raster for odd raster

Purdue University 89 ProcessProcess forfor InkjetInkjet PrinterPrinter IdentificationIdentification

De-skewing, Textural Scanning Softcopy feature Feature Printed version of segmentation Extracted calculation document the document characters space

Test stability of Eliminate BAD the feature within feature a printer model

GOOD

Discriminant analysis

Selected feature set for classification

Purdue University 90 SampleSample TestTest CharactersCharacters ScannedScanned atat 24002400 dpidpi

Cannon S330 Cannon S330 HP 3420 Epson C62 (High) (Standard) HP 3420 Epson C62 (Best) (Normal) (BestPhoto) (Text&Image)

Purdue University 91 PrintersPrinters InstalledInstalled inin thethe PrinterPrinter BankBank

Make Model Mode HP 3420 Normal HP 3650 Normal HP 1315 Normal Lexmark Z25 Better Lexmark Z2250 Normal Canon S330 Standard

Purdue University 92 FeatureFeature ScatterScatter PlotPlot

HP 3420 3.4 HP 3650 HP psc1315 3.3 Lexmark Z25 Lexmark Z2250 3.2 Canon S330 , d=2) o 3.1 θ=90 3

2.9 Entrophy (

2.8

2.7

0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 Max. Correlation Coeff. (θ=90o, d=16)

Purdue University 93 FeatureFeature ScatterScatter PlotPlot

0.94

0.93 , d=1) o 0.92

θ=90 0.91

0.9

0.89 HP 3420 0.88 HP 3650 HP psc1315 0.87

Max. Correlation Coeff. ( Max. Correlation Coeff. Lexmark Z25 0.86 Lexmark Z2250 Canon S330 0.85 1 2 3 4 5 6 7 8 9 Contrast (θ=90o, d=2)

Contrast ( =90o, d=2) Purdue University 94 ThanksThanks forfor youryour attention.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]

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

Purdue University 95 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.

Purdue University 96 ReferencesReferences

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