image quality measures

Image Quality Measures

Omar Javed, Sohaib KhanDr. Mubarak Shah

Factors Affecting Registration Performance

• Mission image quality and content• Reference image quality and content• Mission-Reference differences• Viewing geometry• Quality of DEM• Method of registration

Test Images

• 5 image sequences were used as test images – 08 Oct 99 Image Sequence– 13 Oct 99 Image Sequence– 15 Oct 99 Image Sequence– 16 Oct 99 Image Sequence– 19 Oct 99 Image Sequence

In This Presentation...

• Factors affecting registration performance. • Image quality and content measures

– SNR estimation – Texture measures– Gabor filters

Properties of Mission Imagery Affecting Registration Performance

• Scene Content– Homogenous texture i.e. no distinctive features– Example Images


• Aperture Problem– Presence of roads or homogenous elongated

features causes error in registration along the direction of elongation


• Extreme blur


• Spurious weather phenomenon e.g. clouds, haze ..

Image Quality and Content Measures

• SNR estimation• Texture Measures• Gabor Filters

Blind SNR Estimation• A method to estimate the quality of image is

based on quantityQ=2

fi dr

• The intensity image fi can be modeled by a mixture of Rayleigh pdfs

)2

(

12

22

2

)( i

rm

i ii erwrfi

Algorithm For SNR Estimation• Compute the horizontal and vertical

derivatives of the image• Calculate the gradient magnitude ‘ΔΙ’ from

the derivatives.• Obtain a Histogram of gradient intensity

values from ΔΙ.• Count the number of pixels > 2μ , where μ

is mean of ΔΙ .• Normalize by total number of pixels.

ResultsResults

• 08 Oct 99 SequenceTotal Images = 70

Images with error=12

Unregistered Images=17

Images identified by metric as unregisterable=20

# of false +ves=11

# of false -ves= 20

Misclassification Error= 44.28%

ResultsResults





# of false +ves=9

# of false -ves= 17


ResultsResults


Images with error=6



# of false +ves=0

# of false -ves= 6


ResultsResults





# of false +ves=10

# of false -ves= 44


ResultsResults





# of false +ves=17

# of false -ves= 35


Discussion of Results• Images labeled as low quality

– Red squares indicates large registration error or exclusion from registration

Discussion of Results• Images labeled as high quality


Suitability as an Image Metric• Advantages

– Extreme blur is detected and corresponds well with registration error.

– Low computation time

• Disadvantages– Cloud detection is not robust.– Feature less images are a major cause of

registration error. SNR is not able to detect these images robustly.

Texture

• Gray Level Co-occurrence Matrices (GLCMs)– 2D histogram which encodes spatial relations

• parameters: direction, distance,quantization-levelwindow-size

– Measures are computed on the GLCM• entropy, contrast, homogeneity, energy

Computing GLCM• A GLCM P[i,j] is defined by

– specifying displacement vector d=(dx,dy)– Counting all pairs of pixels separated by d having

gray levels I and j.Input image

Window size

i

j

Distance and DirectionRelationship d

1 ……… i ………. 255

1 ….. j …

……

. 255

+1

P(i, j)

Quantization level

GLCM Measures

• Entropy – Randomness of gray level distribution

• Energy:– uniformity of gray level in a region

i j

jiPjiPEntropy ],[log],[

],[ 2 jiPEnergyi j

GLCM Measures

• Contrast– Measure of difference between gray levels

• Homogeneity– Measure of similarity of texture

],[)( 2 jiPjiContrasti j

i j jijiPyHomogeneit

1],[

Contrast

100 200 300

100

200

300

400100 200 300

100

200

300

400

100 200 300

100

200

300

400100 200 300

100

200

300

400

Entropy

Homogeneity Energy

Contrast

GLCM measures

ResultsResults





# of false +ves=5

# of false -ves= 15


ResultsResults





# of false +ves=7

# of false -ves= 26


ResultsResults


Images with error=6



# of false +ves=15

# of false -ves= 6


ResultsResults





# of false +ves=13

# of false -ves= 41


ResultsResults





# of false +ves=18

# of false -ves= 8


Discussion of Results• Images labeled as low quality


Discussion of Results• Images labeled as high quality



– Homogeneous texture is detected though detection is not robust.

• Disadvantages– It is difficult to fine tune the several parameters

of GLCM’s so that consistent results are obtained for a variety of images.

– Clouds are not detected.– Blur is not detected.

Gabor Filter

• The Gabor function

– is a complex sinusoid centered at frequency (U,V) modulated by a Guassian envelop .

• Gabor function can discriminate between textures

)(22)(

22

22

21),,,,( VyUxj

yx

eeVUyxh

Gabor Filter

• Experiments were done with the following values– Variance of Guassian = 30– Four Gabor kernels

• 1 Horizontal• 1 Vertical• 2 Diagonal

Gabor Kernels

Calculation of Quality metric

• Normalize image intensity values (0 to 255).– Calculate mean of intensity values.– Subtract mean from all intensity.– Add 128 (middle value).

• Determine Gabor response of the image.– Generate four Gabor kernels.– Convolve each kernel with the image.– Multiply the four results.


• Perform connected component analysis and clean up small areas of response.

• Count the number of pixels Np in the response area. Normalize by total number of pixels.

• If Np <Tlow label image as low quality.• If Np >Thigh label image as high quality.


• If both the previous conditions are not met then calculate spatial covariance of Gabor response.

• If spatial covariance is < Ts label image as low quality otherwise label image as high quality.

Results

• Images of Gabor response

Results

• Result after convolution from vertical kernel

Results

• Result after convolution from horizontal kernel

Results

• Result after convolution from diagonal kernel

Results

• Results after multiplication and thresholding

Results

• Images of Gabor response

ResultsResults





# of false +ves=2

# of false -ves= 5


ResultsResults





# of false +ves=4

# of false -ves= 21


ResultsResults

• False +ves

• Difficulty– Correct Detection

ResultsResults

• False -ves

ResultsResults


Images with error=6



# of false +ves=0

# of false -ves= 6


ResultsResults





# of false +ves=7

# of false -ves= 22


ResultsResults





# of false +ves=22

# of false -ves= 10


Results

• A Sample of Images labeled as low quality– Featureless images

Results

• A Sample of Images labeled as low quality– Cloudy Images

– Blur

Results

• A Sample of Images labeled as high quality


– Accurate estimation of amount of texture in an image.

– It can identify hazy, cloudy or featureless images.

– Prediction of success/failure of registration possible.

• Disadvantages– High computation time.

image quality measures

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