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Outline

Outline. Motivations Analytical Model of Skew Effect and its Compensation in Banding and MTF Characterization Moir é Artifact Prediction and Reduction in a Variable Data Printing Environment Conclusions References. Moiré Artifacts in Printing. Moiré due to halftoning process.

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Outline

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  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

  2. 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

  3. Quality of Embedded Images Example: Moiré Artifact Business Week, April 30, 2007 p.56

  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

  5. 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 Causes and Difficulties to Detect Moiré Artifacts in VDP

  6. Phases and Components of Automatic Workflow[3]

  7. 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 Spectrum of Halftoned Digital Image in Terms of Spectrum of Original Continuous-tone Image

  8. Illustration of Halftone Spectrum for a Sine Wave Image Halftone image Continuous-tone input image Screening Compare Threshold matrix Spectrum of the continuous-tone input image Spectrum of the halftone image Frequency doubling effect Frequency of the original sinusoidal wave

  9. |P[1;a]| l T C’ B’ A’ a P[1;f[l]] l T C B A f[l] 0 1 Nonlinear Transformation Due to Halftone |P[0,0;a]| |P[0,1;a]| |P[0,2;a]|

  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

  11. Moiré Artifact as Result of Frequency Doubling Effect Halftone image Continuous-tone input image Screening Compare 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

  12. Moiré Prediction Offline press characterization process Real-time analysis of images in document Test Pattern Digital Press Image Database Human Visual System Model Image Analysis Press Profile Detection Algorithm Moiré Map

  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 MIF Halftone bullseye test pattern with moiré artifacts

  14. Moiré Inducing Frequency (MIF) Detection on Test Page moiré artifacts • 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

  15. 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 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

  16. 1-D illustration Image: 5 cycles per inch Screen: 10 cycles per inch Average: 0.375 Same frequency Average: 0.4667

  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

  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

  19. Printer MIF Detection Result Maximal frequency: 90 cycles/inch Maximal frequency: 55 cycles/inch 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

  20. MIF Detection on Test Page

  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

  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

  23. Indices Representing MIF in Frequency Domain • Check for MIF on the 2D-DSFT of the digital image:

  24. Confidence Measurement in Frequency Domain Power spectrum • In frequency domain, calculate a confidence measure in the neighborhood of the peaks

  25. Confidence Measure Power Spectrum • Strong peak in power spectrum at the MIF location means perceptible moiré is likely to occur in printing • Confidence measure helps to reduce misclassification

  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

  27. Misclassification Due to Strong Edges

  28. Measure Length and Width of Each Detected Region Projection to obtain width • 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. region identified in moiré mask

  29. Misclassification Regions Removed

  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

  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

  32. Results: Hotel Original digital image Scan of the original image print-out Scan of the scaled image print-out Moiré mask

  33. Results: Kodak Window Scan of the original image print-out Original digital image Moiré mask Scan of the scaled image print-out

  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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