Transcending PSNR: 

SIVV as an Image Fidelity Me

John M. Libert

Shahram Orandi

John Grantham

November 30, 2012
Problem:

• Measure change in an image due to so

process or compare effects of different processes on an image

• Spatial(pixel) domain• Frequency domain
• Peak Signal-to-Noise Ratio (PSNR)

• Root Mean Squared Error (RMSE)• Structural Similarity Index (SSIM)

Solution (spatial domain):
Solution (frequency domain)

• NIST Spectral Image Validation Verifi

(SIVV) Metric
SIVV Procedure:

Input Img. 2D Log Power Spectrum

 u

 v

-500

0 500

-400-200

0

200400

Polar Log Power Spectrum





0 50 100 150

0

50

100150200250300350

0 0.1 0.2 0.3 0.4 0

10

-8

10

-6

10

-4

10

-2

100

 Log Power (db)

 Frequency (cycles/pixel)

 Log Power Spectrum

Step 1 : 2D normalized power spectrum image encodes frequency structure in adirections
Input Img. 2D Log Power Spectrum

 u

 v

-500 0 500

-400-200

0

200400

Polar Log Power Spectrum





0 50 100 150

0

50

100150200250300350

0 0.1 0.2 0.3 0.4 0.5

10

-8

10

-6

10

-4

10

-2

10

0

 Log Power (db)

 Frequency (cycles/pixel)

 Log Power Spectrum

Step 2: Polar transform of 2D spectrum simpl
Input Img. 2D Log Power Spectrum

 u

 v

-500 0 500

-400-200

0

200400

Polar Log Power Spectrum





0 50 100 150

0

50

100150200250300350

0 0.1 0.2 0.3 0.4 0.5

10

-8

10

-6

10

-4

10

-2

10

0

 Log Power (db)

 Frequency (cycles/pixel)

 Log Power Spectrum

Step 3: Sum over angle and rescale pixels to frequency to get 1D spectral summary signal for SIVV
Input Img.

0 0.2 0.4

0.40.50.60.70.80.9

1

 Log Power (db)

 Frequency (cycles/pi

 Log Power Spectru

• Peak structure is diagnostic feature

• Relative amplitude• Frequency location
Problem Images

• Rotation• Translation• Dimension change - loss of row(s), 

column(s)

• SIVV is largely invariant to these artif
Experiments:

• Examine response of metrics over a ra

of

– Rotations– Translations
Rotation

Image 1 Image 2.

Rotation 0.20 Deg.

Image 1 

Rotation 5.00 Deg
Translation

Image 1 Image 2.

Translation (x,y) 2 pixels

Image 1 

Translation (x,y) 26 pixe
SIVV - Rotation

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

0.40.50.60.70.80.9

1

 Log Power (db)

 Frequency (cycles/pixel)

SIVV: Rotation 0.20 Deg.

Image 1Image 2

0 0.05 0.1 0.15 0.2 0.25 0.3 0.3

0.40.50.60.70.80.9

1

 Log Power (db)

 Frequency (cycles/pixel)

SIVV: Rotation 5.00 De
SIVV - Translation

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

0.40.50.60.70.80.9

1

 Log Power (db)

 Frequency (cycles/pixel)

SIVV: Translation (x,y) 2 pixels

Image 1Image 2

0 0.05 0.1 0.15 0.2 0.25 0.3 0.3

0.40.50.60.70.80.9

1

 Log Power (db)

 Frequency (cycles/pixel)

SIVV: Translation (x,y) 26 p
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.10.20.30.40.50.60.70.80.9

1

Rotation Angle (degrees)

Values Rescaled 0.0 to 1.0

Effect of Image Rotation on Fidelity

Image RMSESSIMSIVV RMSESIVV Corr
0 5 10 15 20 25

0

0.10.20.30.40.50.60.70.80.9

1

Translation in x and y (pixels)

Values Rescaled 0.0 to 1.0

Effect of Image Translation on Fidelity

Image RMSESSIMSIVV RMSESIVV Corr
Conclusions:

• Where pixel correspondence is likely

– Pixel differencing methods have some ut– Frequency methods such as SIVV can pr

important additional information

• Where pixel correspondence is unlikel

– Frequency analysis may be the best optio
Other SIVV Applications

• Database screening- fingerprint vs. no

fingerprint

• Preprocessor to image quality metric• Live-scan acquisition IV&V• Fingerprint compression-rate and 

downsampling studies

• Fingerprint segmentation
Software:

• SIVV Utility prototyping in MATLAB

– Available upon request from author

• Rewritten in C++ using OpenCV libra

– Win32 and 64– Linux (and Mac OSX)– Released as NBIS 4.1.0 

http://www.nist.gov/itl/iad/ig/nbis.cfm
Selected References

• Libert, J.M.; Grantham, J.; Orandi, S. “A 1D Spectral Image Validation/Verificati

Fingerprints”. NISTIR7599, August 19, 2009. http://www.nist.gov/customcf/get_pdf.cfm?pub_id=903078

• Libert, J.M.; S. Orandi; and J.D. Grantham, “Comparison of the WSQ and JPEG 

Compression Algorithms On 500 ppi Fingerprint Imagery,” NIST Interagency/Int(NISTIR) – 7781, April 23, 2012. http://www.nist.gov/manuscript-publicationsearch.cfm?pub_id=910658

• Z. Wang and A. C. Bovik, “Image quality assessment: from error visibility to stru

similarity,” IEEE Trans. Image Processing, vol. 13, pp. 600 – 612, Apr. 2004.www.ece.uwaterloo.ca/~z70wang/publications/ssim.html
Q&A / Contact Info

John M. Libert

NIST Image Group

John.libert@nist.gov