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An Investigation of Perceived Sharpness and Sharpness Metrics*

An Investigation of Perceived Sharpness and Sharpness Metrics*. Buyue Zhang, Jan P. Allebach School of Electrical and Computer Engineering, Purdue University. Zygmunt Pizlo Department of Psychological Sciences, Purdue University. * This research is sponsored by the Hewlett-Packet Company.

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An Investigation of Perceived Sharpness and Sharpness Metrics*

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  1. An Investigation of Perceived Sharpness and Sharpness Metrics* Buyue Zhang, Jan P. Allebach School of Electrical and Computer Engineering, Purdue University Zygmunt Pizlo Department of Psychological Sciences, Purdue University * This research is sponsored by the Hewlett-Packet Company

  2. Outline • Introduction • Sharpness Metrics • Psychophysical Experiments • Conclusions

  3. Introduction • What is image sharpness? • Resolution • Acutance • Why sharpness and sharpness metrics matter? • Existing sharpness metrics • Depend on the MTF of the system • Require a reference image • Our approach to sharpness metric • Device-independent and no-reference

  4. Outline • Introduction • Sharpness Metrics • Psychophysical Experiments • Conclusions

  5. Sharpening and Blurring Methods • Gaussian Filter and Unsharp Mask Filter • Sharpening/Blurring methods • Vary • Vary λ

  6. Digital Sharpness Scale (DSS) • DSS: the average of the maximal pixel difference in a window centered at each edge pixel

  7. Flower Girl House Motorcyclists Red Barn Rafting Fig. 1 Seven Selected Images. Resolution: 3072 x 2048 Sea Boat

  8. DSS as a Function of Sharpening and Blurring Strength Fig. 2 DSS as a function of

  9. Normal Edge Profile based Sharpness Metrics Fig. 3 (a) Fig.3 (b) • Average Edge Transition Width (AETW) • Average ETW over all edge pixels • Average Edge Transition Slope (AETS) • ETS=AC/ETW • Average ETS over all edge pixels

  10. 1 2 Fig. 4 (b) Subset of Edge Normal Transects on the Edge Map Fig. 4 (a) Lenna.tif Fig. 4 (c) Edge Profiles Extracted from Pixel 1 and 2 in (a)

  11. AETS and AETW vs. λ AETS Fig. 5 (a) AETS vs.λ Fig. 5 (b) AETW vs.λ

  12. Outline • Introduction • Sharpness Metrics • Psychophysical Experiments • Conclusions

  13. Psychophysical Experiments • Psychophysics • Study perception by analyzing the response of subjects to visual stimuli • Four Psychophysical Experiments • Rank order test • Sharpness equality test • Detection test • Preference test

  14. Rank Order Test • Objective • How well do DSS, AETS and AETW agree with perceived sharpness? • Experimental design • Four image contents: Girl, House, Rafting, Sea Boat • Sharpen images with to 8~10 different levels • Images are printed with inkjet printer • “Arrange images according to your ranking of sharpness” • Six subjects

  15. Scatter Plots and Spearman Rank Correlation Coefficients rs rs=0.98 rs=0.97 rs=0.99 rs=0.94 Fig. 6

  16. Detection Test • Objective • What is the perceptual threshold for detecting sharpness increase? • Experimental Design • Test image set: eight different levels in sharpness • Reference image: the original image • “Is the test image sharper or the same compared with the reference image?” • Seven image contents in Fig. 1 • Twenty-one subjects

  17. Psychometric Functions (a) (b) Fig. 7 Two Typical Psychometric Functions from the Detection Test (Mu is the detection threshold for an image or an individual subject)

  18. Preference Test • Objective • What is the observers' preferred level of sharpness? • Experimental Design • Seven image contents, Fig. 1 • For each content: • nine different levels of sharpness, • including the original image • “Select the one image with your most preferred level of sharpness.” • Twenty-one subjects

  19. (a) (b) (c) (d) Fig. 8 The Detection Thresholds vs. the Sharpness Preferences

  20. Outline • Introduction • Sharpness Metrics • Psychophysical Experiments • Conclusions

  21. Conclusions • We proposed three new sharpness metrics: DSS, AETS, and AETW • The sharpness metrics are well correlated with perceived sharpness • The average preferred level of sharpness is consistently higher than the detection threshold across image contents as well as across observers • The average preference has much more variation than the detection threshold across subjects and images. Compared with the detection threshold

  22. Thank You!

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