50. Measuring image artifacts - moire artifacts - 2011ece638/lectures/50...Final Exam May 16th 2008...

HP-PURDUE-CONFIDENTIAL Final 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


Moiré Artifacts in Printing

Moiré due to halftoning process

Test pattern used to characterize halftoning processing of press

Example image to be printed showing moiré artifacts


Quality of Embedded Images Example: Moiré Artifact

Business Week, April 30, 2007 p.56


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


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


Phases and Components of Automatic Workflow[3]


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


Illustration of Halftone Spectrum for a Sine Wave Image

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

Frequency doubling effect

Frequency of the original sinusoidal

wave


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

A B C

A’ B’

C’


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


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


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 document Offline press characterization process


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 MIF Halftone bullseye test pattern with moiré artifacts


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


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


1-D illustration Image: 5 cycles per inch Screen: 10 cycles per inch

Average: 0.375

Average: 0.4667

Same frequency


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


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


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


MIF Detection on Test Page Radial 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


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


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


Indices Representing MIF in Frequency Domain

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


Confidence Measurement in Frequency Domain

  In frequency domain, calculate a confidence measure in the neighborhood of the peaks

Power spectrum


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


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


Misclassification Due to Strong Edges


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


Misclassification Regions Removed


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


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


Results: Hotel Original digital image

Moiré mask

Scan of the original image print-out

Scan of the scaled image print-out


Results: Kodak Window Original digital image

Moiré mask

Scan of the original image print-out

Scan of the scaled image print-out


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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50. Measuring image artifacts - moire artifacts - 2011ece638/lectures/50...Final Exam May 16th 2008...

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