MicroUniformity: A versatile image quality metric Bimal Mishra and Rene Rasmussen Xerox Wilson Center for Research & Technology Slides presentation at IS&T’s PICS 2000 Conference
Acknowledgements • Xerox and Fuji-Xerox image quality standards team – E. Dalal, K. Natale-Hoffman, P. Crean – F. Nakaya, M. Sato • Product program people and other “beta-testers” – S. Reczek, S. Kuek, M. Rabbani, S. Zoltner
Tone levels Gloss Color rendition Lines Micro Text uniformity Macro Pictorial uniformity sharpness Adjacency
Micro Uniformity
DAC Color Printer IQ Attributes Key contributors: – Seemingly random variations • Process noise • Stochastic screens, etc. – Periodic variations • Amplitude modulated screens • High-frequency moire See E. Dalal et al., PICS-98
Development of an instrumented metric • Technology comparison; competitive benchmarking – Current evaluation is conducted visually by expert panel – Automate evaluation of DAC Micro Uniformity – Did not have metric suitable for all technologies • Engineering tool to assess, independently: – Appearance of process noise (graininess) – Appearance of halftone patterns – Appearance of moire patterns – and without being affected by • streaks • bands – … … ...
Description of the metric
Traditional graininess metric • Analysis of photopic density fluctuations recorded with a long (2-3mm) and narrow (10-20um) scanning slit. • Amplitude spectrum modulated with human Contrast Sensitivity Function [CSF], and integrated over frequencies. • Density factor exp(-1.8D) applied. • Issue: Halftone screen can dominate the measure
Filtering out the halftone frequency • Aperture filtered halftone graininess – Slit width set to filter out the halftone frequency; M. Maltz et al. 1993 • Processing of the 1D amplitude spectrum – T. Bouk and N. Burningham 1992 • Not feasible in cases of process colors with multiple rotated screens
Micro uniformity metric • Image capture – Drum scanner at 600dpi, RGB – Calibration from RGB to photopic reflectance • Image processing – Separation into three components – Visual filter • Evaluation of uniformity – Independently for the three components • End up with 3 numbers
Image processing Amplitude spectrum Real space Photopic R FFT Phase spectrum Fourier space Kernel Classification operation Binary mask Visually structured/unstructured filtered image Separate, FFT inverse FFT, crop HF unstructured Structured Unstructured Filter image image LF unstructured
Classification of amplitude spectrum f y • Statistical analysis of narrow frequency band classifies each pixel within the band – A(m,n) compared to f x <A>(f) + 3.3*S(f) • Very-low frequency (<0.1c/mm) region classified as unstructured Most simple approach without f-dependency does not work (e.g., 1/f) Frequency band from f to f + Δ f Average amplitude: <A>(f) Standard deviation of amplitude: S(f)
Single-number evaluation of each of the 3 image components • Options – Amplitude spectrum integration • Suitable when phases are uncorrelated – Space-domain integration • Suitable also for localized defects (strong phase correlation)
…evaluation of image components • Space-domain integration – Boundary region may contain artifacts and is cropped away – RMS of deviation from the mean – Apply lightness factor scaling • Outputs from the analysis: – “Visual Structure” [VS] • Screen, high frequency moire, isolated streaks, periodic bands – “Visual Noise High Frequency ” [VNHF] • Process noise, stochastic screens, “graininess” – “Visual Noise Low Frequency ” [VNLF] • Mottle
Examples
Print samples 20mm Combinations of Plus substrate and mode variations 50% C,M,Y,K
Diversity of “looks”
Examples 1. Multiple rotated screens 2. Low frequency cluster dot screen 3. Frequency modulated screen 4. Rotated screen with strong rosette pattern 5. Streaks 6. Moire pattern Next
Multiple rotated screens VisualNoise HF Visual Structure 121-8
Multiple rotated screens, 121-8
Multiple rotated screens, 121-8 VNHF value VisualNoise HF contrast enhanced
Multiple rotated screens, 121-8 VS value Examples Visual Structure contrast enhanced
Cluster dot screen, low-frequency VisualNoise HF Visual Structure
Cluster dot screen, low-frequency
Cluster dot screen, low-frequency VisualNoise HF contrast enhanced
Cluster dot screen, low-frequency Examples Visual Structure contrast enhanced
Frequency modulation screen
Frequency modulation screen VisualNoise HF contrast enhanced
Frequency modulation screen Examples Visual Structure contrast enhanced
Rotated screens, strong rosette pattern; 70-6
Rotated screens, strong rosette pattern; 70-6 VisualNoise HF contrast enhanced
Rotated screens, strong rosette pattern; 70-6 Examples Visual Structure contrast enhanced
Removal of streaks from graininess measurement Unprocessed image Unstructured Structured (Visual filter NOT applied here) Examples
Moire pattern Uncoated paper Coated paper, stronger moire pattern Visual Structure
… moire pattern Unprocessed image Structured image Works for 1D structure Examples at an angle
Preliminary correlation to visual evaluations
Inspection of separated images • Visual inspection (not IQ assessment) – Good qualitative agreement between the separated images and visual assessment of print samples • Weak artifacts can be seen in the “structured image” in cases with little or no visible screen, but always lead to VS < 1 (not perceptible) • Comparison to traditional graininess metric – Good agreement between VNHF and graininess metric on samples where it can be expected
Comparison to visual demerit ratings • Visual demerit ratings – Performed by “expert panel” – Overall evaluation of the 10 color patches – 0 represent “perfection”, larger values are worse • Analysis based on metric – VNHF = RMS of deviation from mean of the high-frequency un-structured image – Aggregate over the 10 patches taken as the RMS of the 10 VNHF values – S-curve fitted to map to demerit scale
…comparison to visual Xerox C55 15 Ink jet, plain S-Curve(VNHF) y = 1.0177x - 0.7721 Linear.(S-Curve(VNHF)) paper, draft Visual Demerit Rating Other than noise 11 R ² = 0.9358 8 4 0 -4 0 4 8 11 15 Predicted Demerit Pictrography Apple CLW Dai-Nippon Epson PM750 Xerox DC40 photo mode
Summary • Applications for engineering diagnostics – The metric can automatically separate key factors that contribute to an overall measure of micro uniformity • Application for competitive benchmarking – Results are promising with respect to prediction of overall visual micro uniformity rating, by combination of structured and unstructured variations
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