Image Quality Measures

1. Image Quality Measures Omar Javed, Sohaib Khan Dr. Mubarak Shah

2. 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

3. 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

4. In ThisPresentation... • Factors affecting registration performance. • Image quality and content measures • SNR estimation • Texture measures • Gabor filters

5. Properties of Mission Imagery Affecting Registration Performance • Scene Content • Homogenous texture i.e. no distinctive features • Example Images

6. Properties of Mission Imagery Affecting Registration Performance • Aperture Problem • Presence of roads or homogenous elongated features causes error in registration along the direction of elongation

7. Properties of Mission Imagery Affecting Registration Performance • Extreme blur

8. Properties of Mission Imagery Affecting Registration Performance • Spurious weather phenomenon e.g. clouds, haze ..

9. Image Quality and Content Measures • SNR estimation • Texture Measures • Gabor Filters

10. Blind SNR Estimation • A method to estimate the quality of image is based on quantity Q=2fi dr • The intensity image fi can be modeled by a mixture of Rayleigh pdfs

11. 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.

12. Results Results • 08 Oct 99 Sequence Total 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%

13. Results Results • 13 Oct 99 Sequence Total Images = 84 Images with error=18 Unregistered Images=11 Images identified by metric as unregisterable=21 # of false +ves=9 # of false -ves= 17 Misclassification Error= 30.95%

14. Results Results • 15 Oct 99 Sequence Total Images = 115 Images with error=6 Unregistered Images=0 Images identified by metric as unregisterable=0 # of false +ves=0 # of false -ves= 6 Misclassification Error= 5.21%

15. Results Results • 16 Oct 99 Sequence Total Images = 169 Images with error=19 Unregistered Images=39 Images identified by metric as unregisterable=25 # of false +ves=10 # of false -ves= 44 Misclassification Error= 31.95%

16. Results Results • 19 Oct 99 Sequence Total Images = 172 Images with error=15 Unregistered Images=22 Images identified by metric as unregisterable=19 # of false +ves=17 # of false -ves= 35 Misclassification Error= 30.23%

17. Discussion of Results • Images labeled as low quality • Red squares indicates large registration error or exclusion from registration

18. Discussion of Results • Images labeled as high quality • Red squares indicates large registration error or exclusion from registration

19. 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.

20. Texture • Gray Level Co-occurrence Matrices (GLCMs) • 2D histogram which encodes spatial relations • parameters: direction, distance, quantization-level window-size • Measures are computed on the GLCM • entropy, contrast, homogeneity, energy

21. i Distance and Direction Relationship d j 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. P(i, j) Input image 1 ….. j ………. 255 +1 1 ……… i ………. 255 Quantization level Window size

22. GLCM Measures • Entropy • Randomness of gray level distribution • Energy: • uniformity of gray level in a region

23. GLCM Measures • Contrast • Measure of difference between gray levels • Homogeneity • Measure of similarity of texture

24. Contrast Entropy Energy Homogeneity GLCM measures Contrast

25. Results Results • 08 Oct 99 Sequence Total Images = 70 Images with error=12 Unregistered Images=17 Images identified by metric as unregisterable=19 # of false +ves=5 # of false -ves= 15 Misclassification Error= 28.57%

26. Results Results • 13 Oct 99 Sequence Total Images = 84 Images with error=18 Unregistered Images=11 Images identified by metric as unregisterable=12 # of false +ves=7 # of false -ves= 26 Misclassification Error= 39.28%

27. Results Results • 15 Oct 99 Sequence Total Images = 115 Images with error=6 Unregistered Images=0 Images identified by metric as unregisterable=15 # of false +ves=15 # of false -ves= 6 Misclassification Error= 18.26%

28. Results Results • 16 Oct 99 Sequence Total Images = 169 Images with error=19 Unregistered Images=39 Images identified by metric as unregisterable=32 # of false +ves=13 # of false -ves= 41 Misclassification Error= 31.95%

29. Results Results • 19 Oct 99 Sequence Total Images = 172 Images with error=15 Unregistered Images=22 Images identified by metric as unregisterable=47 # of false +ves=18 # of false -ves= 8 Misclassification Error= 15.11%

30. Discussion of Results • Images labeled as low quality • Red squares indicates large registration error or exclusion from registration

31. Discussion of Results • Images labeled as high quality • Red squares indicates large registration error or exclusion from registration

32. Suitability as an Image Metric • Advantages • 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.

33. 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

34. Gabor Filter • Experiments were done with the following values • Variance of Guassian = 30 • Four Gabor kernels • 1 Horizontal • 1 Vertical • 2 Diagonal

35. Gabor Kernels

36. 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.

37. Calculation of Quality metric • 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.

38. Calculation of Quality metric • 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.

39. Results • Images of Gabor response

40. Results • Result after convolution from vertical kernel

41. Results • Result after convolution from horizontal kernel

42. Results • Result after convolution from diagonal kernel

43. Results • Result after convolution from diagonal kernel

44. Results • Results after multiplication and thresholding

45. Results • Images of Gabor response

46. Results • Images of Gabor response

47. Results • Images of Gabor response

48. Results • Images of Gabor response

49. Results • Images of Gabor response

50. Results Results • 08 Oct 99 Sequence Total Images = 70 Images with error=12 Unregistered Images=17 Images identified by metric as unregisterable=26 # of false +ves=2 # of false -ves= 5 Misclassification Error= 10.00%