Image Quality Measures

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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

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

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

Distance and DirectionRelationship d

1 ……… i ………. 255

1 ….. j …

……

P(i, j)

Quantization level

GLCM Measures

• Entropy – Randomness of gray level distribution

• Energy:– uniformity of gray level in a region

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

Contrast

100 200 300

400100 200 300

100 200 300

400100 200 300

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)(

21),,,,( VyUxj

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

• Result after convolution from diagonal kernel

Results

• Results after multiplication and thresholding

Results

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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