an investigation of perceived sharpness and sharpness metrics*
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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. - PowerPoint PPT PresentationTRANSCRIPT
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An Investigation of Perceived Sharpness and Sharpness Metrics*
Zygmunt PizloDepartment of Psychological Sciences,
Purdue University
* This research is sponsored by the Hewlett-Packet Company
Buyue Zhang, Jan P. AllebachSchool of Electrical and Computer Engineering,
Purdue University
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Outline
Introduction
Sharpness Metrics
Psychophysical Experiments
Conclusions
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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
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Outline
Introduction
Sharpness Metrics
Psychophysical Experiments
Conclusions
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Sharpening and Blurring Methods
[ , ] [ ]** [ ] (2)by m n g m,n x m,n
[ , ] (1 ) [ ] [ ] (3)USM by m n x m,n - y m,n
Gaussian Filter and Unsharp Mask Filter
(1) 2
1 ][
2
22
2
)(
nm
em,ng
Sharpening/Blurring methods Vary
Vary λ
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Digital Sharpness Scale (DSS)
DSS: the average of the maximal pixel difference
in a window centered at each edge pixel
2 2
22 2
22 2
2[ , ] (1 ) (4)
2
m nm n
LoG m n e
[ , ] [ , ]** [ , ] (5)LoGy m n x m n LoG m n
(6) |)](min[)](max[| 1
),(
edgeyInm
LoGLoGedge
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DSS
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Flower Girl House
Motorcyclists Red Barn Rafting
Sea Boat
Fig. 1 Seven Selected Images. Resolution: 3072 x 2048
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DSS as a Function of Sharpening and Blurring Strength
Fig. 2 DSS as a function of
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Normal Edge Profile based Sharpness Metrics
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
Fig. 3 (a) Fig.3 (b)
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Fig. 4 (c) Edge Profiles Extracted from Pixel 1 and 2 in (a)
Fig. 4 (a) Lenna.tif Fig. 4 (b) Subset of Edge Normal Transects on the Edge Map
1
2
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AETS and AETW vs. λ
Fig. 5 (b) AETW vs.λFig. 5 (a) AETS vs.λ
AE
TS
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Outline
Introduction
Sharpness Metrics
Psychophysical Experiments
Conclusions
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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
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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
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Scatter Plots and Spearman Rank Correlation Coefficients rs
Fig. 6
rs=0.97
rs=0.99
rs=0.98
rs=0.94
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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
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Psychometric Functions
Fig. 7 Two Typical Psychometric Functions from the Detection Test
(Mu is the detection threshold for an image or an individual subject)
(a) (b)
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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
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Fig. 8 The Detection Thresholds vs. the Sharpness Preferences
(a) (b)
(c) (d)
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Outline
Introduction
Sharpness Metrics
Psychophysical Experiments
Conclusions
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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
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Thank You!