1. Color Image Quality Assessment Part II: Image Quality Metrics
Marius Pedersen
The Norwegian Colour and Visual Computing Laboratory, Gjøvik University College, Gjøvik, Norway
Jan P. Allebach
School of ECE, Purdue University
West Lafayette, Indiana

2. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

3. What is an image quality metric?
An objective mathematical way to calculate quality without asking observers.
Measure of quality
Image
Metric

4. Different types of metrics
Three main types of metrics:
Full-reference.
No-reference.
Reduced-reference.
Reproduction
Measure of quality
Metric
Original

5. Existing image quality metrics
Metrics usually follow a common framework.
Different stages:
Unless stated otherwise we focus on full-reference
Many
Fewer
Original and reproduction
Color space transforms
Human visual system models
Quality calculation
Pooling
Quality value

6. Colour space transforms
Preparation for applying a model of the Human Visual System (HVS).
This step is a tranformation from RGB (or another colour space) into a more suitable space.
This space is usually adapted to the filtering, where a better representation of the perception of colour is achieved.
For example an opponent colour space.
Color space transforms

7. Human visual system models
These models usually simulate low level features of the HVS, such as contrast sensitivity functions (CSFs) or masking.
Other possibilites are high-level features, such based on the idea that our human visual system is adapted to extract information or structures from the image.
Human visual system models

8. Quality calculation Usually quality calculation is a distance.
Assumes that the original has the highest quality.
Euclidean distance
Done in a perceptually uniform color space.
Nonlinearly transformed color space so that distance is proportional to ones ability to perceive changes in color.
Recently, CIELAB most commonly used.
Distance = quality B A
Quality calculation
25/09/12 Eq. from

9. Pooling Pooling is the reduction of quality values.
Many
Pooling is the reduction of quality values.
Quality map reduced to fewer values.
Values from different metrics to an overall value.
Motivation: Easier to manage one value than many.
Most metrics pool by taking the average.
Fewer
Pooling
Quality map
Pooling

10. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

11. Classification of metrics
Metrics can be classified into several categories:
Mathematically based metrics.
MSE or ∆ 𝐸 𝑎𝑏 ∗
operate only on the intensity of the distortions.
M Pedersen, JY Hardeberg. Full-Reference Image Quality Metrics: Classification and Evaluation. Foundations and Trends® in Computer Graphics and Vision 7 (1), 1-80

12. Classification of metrics
To understand the metrics we propose a classification of them into:
Mathematically based metrics.
MSE or ∆ 𝐸 𝑎𝑏 ∗
Low-level based metrics.
S-CIELAB or S-DEE.
take into account the visibility of the distortions using low-level models of the human visual system.
M Pedersen, JY Hardeberg. Full-Reference Image Quality Metrics: Classification and Evaluation. Foundations and Trends® in Computer Graphics and Vision 7 (1), 1-80

13. Classification of metrics
To understand the metrics we propose a classification of them into:
Mathematically based metrics.
MSE or ∆ 𝐸 𝑎𝑏 ∗
Low-level based metrics.
S-CIELAB or S-DEE.
High-level based metrics.
SSIM or VIF.
are based on the idea that our human visual system is adapted to extract information or structures from the image.
M Pedersen, JY Hardeberg. Full-Reference Image Quality Metrics: Classification and Evaluation. Foundations and Trends® in Computer Graphics and Vision 7 (1), 1-80

14. Classification of metrics
To understand the metrics we propose a classification of them into:
Mathematically based metrics.
MSE or ∆ 𝐸 𝑎𝑏 ∗
Low-level based metrics.
S-CIELAB or S-DEE.
High-level based metrics.
SSIM or VIF.
Other metrics.
VSNR or CISM.
are either based on other strategies or combine two or more of the above groups.
M Pedersen, JY Hardeberg. Full-Reference Image Quality Metrics: Classification and Evaluation. Foundations and Trends® in Computer Graphics and Vision 7 (1), 1-80

15. Mathematically based metrics: MSE
MSE is a mathematically based metric; it calculates the cumulative squared error between the original image and the distorted image.
MSE is given as:
where x and y indicate the pixel position, M and N are the image width and height.
These simple mathematical models are usually not well correlated with perceived image quality.
Still been of influence to other metrics.

16. Mathematically based metrics: ∆ 𝐸 𝑎𝑏 ∗
Metrics measuring color difference also belong to the group of mathematically based metrics.
Lr,ar,br is the sample color and Lo,ao,bo is the reference color in CIELAB.
∆ 𝐸 𝑎𝑏 ∗ has served as a satisfactory tool for measuring perceptual difference between uniform color patches

17. Mathematically based metrics: ∆ 𝐸 𝑎𝑏 ∗
∆ 𝐸 𝑎𝑏 ∗ has also been used to measure natural images, where the color difference of each pixel of the image is calculated.
The mean of these differences is the overall indicator:

18. Example mathematically based metrics
Original image from R. Halonen, M. Nuutinen, R. Asikainen, and P. Oittinen. Development and measurement of the goodness of test images for visual print quality evaluation. In S. P. Farnand and F. Gaykema, editors, Image Quality and System Performance VII, volume 7529, pages –1–10, San Jose, CA, USA, Jan SPIE.

19. Example – image difference maps

20. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

21. Low-level based metrics
Low-level based metrics simulates the low level features of the HVS, such as contrast sensitivity functions (CSFs) or masking.
Contrast sensitivity is a measure of the ability to discern between luminance of different levels in a static image.

22. Typical CSF functions As introduced in the first part.
CSF varies with many physical attributes:
spatial frequency,
orientation,
light adaptation level,
image area,
viewing distance,
retinal eccentricity.
retinal eccentricity is a measure of how far away a given point in the visual field is from the fixation point (fovea). It is measured in degrees of retinal eccentricity. 
Figure from C. A. Bouman: Digital Image Processing - January 9, 2012 (25/09/12:

23. Low-level based metrics: S-CIELAB
∆ 𝐸 𝑎𝑏 ∗ was not correlated with perceived image difference.
Zhang and Wandell proposed a spatial extension based on ∆ 𝐸 𝑎𝑏 ∗
They had two goals:
a spatial filtering to simulate the blurring of the HVS.
consistency with the basic CIELAB calculation for large uniform areas.
Zhang, X. & Wandell, B. A. A spatial extension of CIELAB for digital color image reproduction. Proc. Soc. Inform. Display 96 Digest, Soc. Inform. Display 96 Digest, 1996,

24. Low-level based metrics: S-CIELAB
Color separation:
Image transformed into the O1O2O3 opponent color space.
Spatial filter:
Data in each color channel is filtered by a 2-dimensional separable spatial kernel.
Color difference:
CIELAB color space
∆ 𝐸 𝑎𝑏 ∗ to calculate color differences.
Pooling:
Usually taking the average.
Figure from 14/09/12

25. S-CIELAB CSFs
Figure from Johnson, G. M. & Fairchild, M. D. Darwinism of Color Image Difference Models. Color Imaging Conference, 2001,

26. Example S-CIELAB
Want to test S-CIELAB? Matlab code available online at
Loading Hats and HatsCompressed
load images/hats
load images/hatsCompressed
Define viewing conditions:
We choose two different conditions
SPD = 23 (18in/72dpi) and SPD = 56 (44.5in/72dpi)
SPD(DPImonitor/((180/pi)*atan(1/NoINCH)))
Run S-CIELAB code

27. Example S-CIELAB - maps
18 inches viewing distance
Mean=3.4, Min=0.4, Max=52.6, median=2.4
44.5 inches viewing distance
Mean=2.3, Min=0.02, Max=28.2, median=1.7

28. Other low-level based metrics
Spatial-DEE (S-DEE)
This metric follows the S-CIELAB framework, but ∆ 𝐸 𝑎𝑏 ∗ is replaced with ΔEE.
Spatial filters from Johnson and Fairchild.
Adaptive Bilateral Filter (ABF)
uses a bilateral filter to blur the image, while preserving edges, which is not the case when using CSFs.
Simone, G.; Oleari, C. & Farup, I. PERFORMANCE OF THE EUCLIDEAN COLOR-DIFFERENCE FORMULA IN LOG-COMPRESSED OSA-UCS SPACE APPLIED TO MODIFIED-IMAGE-DIFFERENCE METRICS. 11th Congress of the International Colour Association (AIC), 2009
Wang, Z. & Hardeberg, J. Y. Development of an adaptive bilateral filter for evaluating color image difference. Journal of Electronic Imaging, 2012, 21,

29. Comparison of filtering methods
Different filtering methods: CSFs (S-CIELAB), bilateral filter (from ABF), CSFs in NSCT (Pedersen et al.).
Double check filtering from S-CIELAB
Original
S-CIELAB
ABF
NSCT
Pedersen, M.; Liu, X. & Farup, I.. Improved Simulation of Image Detail Visibility using the Non-Subsampled Contourlet Transform. Color and Imaging Conference, 2013

30. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

31. High level based metrics
High-level based metrics quantify quality based on the idea that our HVS is adapted to extract information or structures from the image.

32. High level based metrics: SSIM
SSIM defines the structural information in an image as those attributes that represent the structure of the objects in the scene, independent of the average luminance and contrast.
Quantifies perceived change in structural information.
Incorporates luminance masking and contrast masking.
Figure from Wang, Z.; Bovik, A. C.; Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13,

33. High level based metrics: SSIM
where μ is the mean intensity for signals x and y, and σ is the standard deviation of the signals x and y. signals x and y are of size MxN.
C is a constant defined as
where L is the dynamic range of the image, and K1<<1. C2 is similar to C1 and is defined as:
where K2<<1. These constants are used to stabilize the division of the denominator.

34. High level based metrics: SSIM
SSIM is calculated for local windows in the image.
A single value is given as:
where X and Y are the reference and the distorted images, 𝑥 𝑗 and 𝑦 𝑗 are image content in local window j, and W indicates the total number of local windows.

35. Example SSIM
Want to test SSIM?
Transform the images to grayscale
In the following example I have used Rgb2gray() in Matlab
Run ssim_index(img,img2)
Using default parameters
K = [ ];
window = ones(8); (window size)
L = 100; (dynamic range)

36. Mean=0.89, Min=0.23, Max=0.995, median=0.92
Example SSIM - maps
Mean=0.89, Min=0.23, Max=0.995, median=0.92

37. Other approaches
Others metrics considered in this group are based on other approaches or metrics combining two or more of the above groups.
Visual Signal to Noise Ratio (VSNR), based on near-threshold and suprathreshold properties of the HVS, incorporating both low-level features and mid-level features.
Color image similarity measure, this can be divided into two parts; one dealing with the HVS and one with structural similarity.
Generalization: S-CIELAB framework + SSIM
Chandler, D. M. & Hemami, S. S. VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images. IEEE Trans. Image Processing, 2007, 16,
J. Lee and T. Horiuchi. Image quality assessment for color halftone images based on color structural similarity. IEICE Trans. Fundamentals, E91A:1392–1399, 2008.

38. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

39. More on HVS modelling – masking
There are additional aspects of the HVS that can be modeled:
Luminance masking
Contrast masking
Masking in sound:
Auditory masking occurs when the perception of one sound is affected by the presence of another sound.

40. Luminance masking
Perception of lightness is a nonlinear function of luminance.
Luminance masking: the luminance of the original image signal masks the variations in the distorted signal.
Visibility threshold increases as background luminance increases
Each image has the same amplitudes but different mean (lowest on the left).
As can be seen, the pattern is more noticeable towards the left.
When the average brightness is higher, the same amount of regional change amounts to a lower contrast as compared to a lower average brightness. Thus the same variation in a bright region would be less visible than in a darker region.
05/10/12:

41. Contrast masking
The reduction in visibility of one image component caused by the presence of another image component with similar spatial location and frequency content is called “contrast masking”.
Contrast masking can occur
within a colour channel,
across channels,
across subbands,
across orientations.

42. Example contrast masking
A test contrast pattern (left) and three different masking contrast patterns (middle).
The sum of the test and masks are shown to the right.
The test pattern is difficult to see when the frequency of the test and mask are similar.
Added Gaussian noise to both.
Both images have the same mean luminance.
Beach image 17/09/15:

43. Pooling – one step further
Pooling is very important for achieving an IQ metric correlated with the percept.
Quality map
Many
Pooling
Fewer
Pooling
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, 2004.
Z. Wang and X. Shang. Spatial pooling strategies for perceptual image quality assessment. In International Conference on Image Processing, pages 2945–2948, Atlanta, GA, Oct IEEE.

44. Type of pooling
Pooling can usually be applied in three different stages:
1) Spatial pooling: combining values in the quality map in the image domain.
Spatial pooling is always needed
Spatial pooling

45. Type of pooling
Pooling can usually be applied in three different stages:
channel pooling: pooling values from different (color) channels.
needed only when the image is decomposed into different channels.
Channel pooling

46. Quality attribute pooling
Type of pooling
Pooling can usually be applied in three different stages:
quality attribute pooling: combining several quality maps generated from different quality attributes (i.e. color, lightness)
Only needed when different quality maps are calculated for each quality attribute.
Quality attribute pooling

47. General formulation of pooling
A general form of a spatial pooling approach is given by
where wi is the weight given to the ith location and mi is the quality measure of the ith location.
M is the pooled quality value.
Most spatial pooling methods can be formulated in this way.
In a simple average pooling method, wi is the same over the image space.
Pooling can be divided into two categories:
Quality based pooling
Content based pooling

48. Quality based pooling
Quality based methods assume that the weights wi are related to the quality value mi at the ith location of the quality map, i.e.
These methods follow the principle that low quality values should be weighted more heavily compared to higher quality values.
Common approaches:
Minkowski pooling
Monotonic function pooling (Wang and Shang)
Percentile pooling (Moorthy and Bovik)
Z. Wang and X. Shang. Spatial pooling strategies for perceptual image quality assessment. In International Conference on Image Processing, pages 2945–2948, Atlanta, GA, Oct IEEE.
A. K. Moorthy and A. C. Bovik. Perceptually significant spatial pooling techniques forimage quality assessment. In D. E. Rogowitz and T. N. Pappas, editors, Human Vision and Electronic Imaging XIV, volume 7240 of Proceedings of SPIE, page , San Jose, CA, Jan 2009.

49. Content based pooling
Content based methods assume that the weights wi might be related to image content in the local region around the ith pixel.
where ci is a measure of perceptual significance of image content in the local region around the ith location.
Assumption: an error that appears on a perceptually significant region is much more annoying than a distortion appearing in an inconspicuous area.
Common methods:
Information-content weighting pooling
Gaze based pooling
Saliency pooling

50. Evaluation of pooling techniques
Comparing the results from different metrics with different pooling methods against perceptual data.
Gong, M. & Pedersen, M. Spatial Pooling for Measuring Color Printing Quality Attributes. Journal of Visual Communication and Image Representation, 2012, 23,
25/09/12:
The overall results indicate that:
Pooling parameters are important.
Pooling is metric dependent.

51. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

52. Introduction: evaluation of metrics
In order to know if an image quality metric correlates with the human percept, some kind of evaluation of the metric is required.
The most common to compare the results of the metrics to the results of human observers.

53. Pair comparison
In pair comparison experiments observers judge quality based on a comparison of image pairs, i.e which image in the pair is the best according to a given criterion
For example which has the highest quality or is the least different from an original.
These experiments can be either
forced-choice, where the observer needs to give an answer, or
the observer is not forced to make a decision and may judge the two reproductions as equals (tie).
No information on the distance between the images is recorded, making it less precise than category judgment, but less complex.
Pair comparison is the most popular method to evaluate e.g. gamut mapping*, and is often preferred due to its simplicity, requiring little knowledge by the user.
* CIE. Guidelines for the evaluation of gamut mapping algorithms. Technical Report ISBN: , CIE TC8-03, 156:2004.

54. Example pair comparison experiment
For the first trial the observer judged the left patch to be closer to the reference, the same with the second trial, and in the third trial the right. The observer judges all combinations of pairs.

55. Category judgement
In category judgment the observer is instructed to judge an image according to a criterion, and the image is assigned to a category.
Five or seven categories are commonly used, with or without a description of the categories.
One advantage of category judgment is that information on the distance between images is recorded, but the task is more complex than pair comparison for the observers.
Category judgment experiments are often faster than pair comparison, with fewer comparisons necessary.

56. Category judgment experiment
Reference
Test set 50 30 50 70
Trial 1 30 40 50 60 70
Categories: 1-7 4 2 1 2 4

57. Rank order
The observer is presented with a number of images, who is asked to rank them based on a given criterion.
Rank order can be compared to doing a pair comparison of all images simultaneously.
If the number of images is high, the task quickly becomes challenging to the observer.
However, it is a fast way of judging many images and a simple type of experiment to implement.

58. Rank order example
The observer ranks the reproductions from best to worst according to a given criteria.

59. b
Reproduction
Original
Correlation
The most common measure of correlation is the Pearson product-moment correlation coefficient
a linear correlation between two variables (X and Y)
The correlation value r is between −1 and +1.

60. Non-linear correlation
Metric scores might not linearly fit the results from observers.
Solution: non-linear fitting.
Sheikh et al. proposed a 5-parameter logistic function:
Various number of parameters used by different researchers.
Overfitting can be a problem.
H.R. Sheikh et al., A statistical evaluation of recent full reference image quality assessment algorithms, IEEE Trans. Image Processing, vol. 15, no. 11, pp , 2006“
Image from (04/07/13)

61. Other performance measures
Rank correlation (Spearman and Kendall Tau)
Root-Mean-Squared-Error
F-statistic for comparing the variance of two sets of sample points.
Outlier ratio (percentage of the number of predictions outside the range of ±2 times of the standard deviations) of the predictions.
Requires access to the individual scores, which is normally not given in databases.
Rank order method (Pedersen and Hardeberg, CGIV, 2007)
Video Quality Experts Group. FINAL REPORT FROM THE VIDEO QUALITY EXPERTS GROUP ON THE VALIDATION OF OBJECTIVE MODELS OF MULTIMEDIA QUALITY ASSESSMENT, PHASE I. 2008
Pedersen, M. & Hardeberg, J. Y. Rank Order and Image Difference Metrics 4th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), IS&T, 2008,

62. Example - evaluation of metrics
Evaluation of metrics is very important to ensure their performance.
Requires a database of images and corresponding subjective scores.
Use an existing database or create a new database.

63. Existing image quality databases
Name
CID:IQ
TID
LIVE (Release 2)
Toyama
CPIQ
IRCCyN/IVC
VAIQ
TUD
JPEGXR
HTI
IBBI
MMSP 3D
A57
WIQ
TID2013
TID2008
CSIQ
DRIQ
IVC
Watermarking
3D image
Art image
TUD1
TUD2
Enrico
Broken Arrows
Fourier Subband
Meerwald
Year
2014
2013
2008
2006
2010
2012
2005
2007
2009
2011
Color or Gray
Color
Gray
Number of reference image 23 25 29 14 30 26 10 5 12 6 8 42 11 9 3 7
Number of distortion type 24 17 2 15 4 1
Number of distortion level X
Number of image
690
3000
1725
808
196
896
104
195
105
130
315
132 96
120
575 16 55 60 54 80
Number of observer
985
838 35
No Specify 20
118 18
Thanks to Xinwei Liu for putting together the table.

64. Our evaluation of metrics
6 state-of-the-art databases.
Compression, gamut mapping, noise, contrast, color, etc.
22 state of the art metrics selected.
SSIM, S-CIELAB, VSNR, SHAME, PSNR, etc.
Compare the results from the observers to the quality values from the metrics.
Correlation as performance measure.
TID2008
Skip this slide?
Dugay
IVC
Ajagamelle
Simone
Pedersen
M Pedersen, JY Hardeberg. Full-Reference Image Quality Metrics: Classification and Evaluation. Foundations and Trends® in Computer Graphics and Vision 7 (1), 1-80

65. Evaluation results Results show that performance depends on:
Images, type of distortion, and magnitude of the distortion.
Metrics perform better for simple and single distortions, and worse for complex and multiple distortions.
Skip this slide?

66. Evaluation CID:IQ database (www.colourlab.no/CID)
60 image quality metrics
Results from 50 cm viewing distance
Compare the results from the observers to the quality values from the metrics.
Correlation as performance measure.
Marius Pedersen. EVALUATION OF 60 FULL-REFERENCE IMAGE QUALITY METRICS ON THE CID:IQ. International Conference on Image Processing (ICIP). 5 pages. September Quebec, Canada.

67. Linear Pearson correlation 50 cm
CID has the highest correlation coefficient, but it not statistically significantly different from many other metrics, such as MAD, WSSI, colorPSNRHA, and VIF.

68. Non-linear Pearson correlation 50 cm
WSSI has the highest Pearson correlation coefficient, but it is not statistically significantly different from MSSIM. The highest performing color metric is CID.

69. Synopsis What is an image quality metric Classification of metrics
Mathematically based metrics
Low-level based metrics
High-level based metrics
Important factors for metrics
Masking
Pooling
Evaluation of metrics
Image quality attributes

70. One metric for overall quality?
Researchers still search for «the holy grail»:
one metric to measure overall quality.
However, image quality is complex, and one metric might not be suitable to measure all aspects.
Solution:
Divide overall quality into quality attributes.
Image quality attributes = terms of perception
Sharpness, contrast, color, etc.

71. Subset of quality attributes – CPQAs
Pedersen et al. proposed six Color Printing Quality Attributes (CPQAs):
Color contains aspects related to color, such as hue, saturation, and color rendition, except lightness.
Lightness is considered so perceptually important that it is beneficial to separate it from the color CPQA. Lightness will range from ”light” to ”dark”.
Contrast can be described as the perceived magnitude of visually meaningful differences, global and local, in lightness and chromaticity within the image.
Sharpness is related to the clarity of details and definition of edges.
In color printing some artifacts can be perceived in the resulting image. These artifacts, like noise, contouring, and banding, contribute to degrading the quality of an image if detectable.
The physical CPQA contains all physical parameters that affect quality, such as paper properties and gloss.
Even though these are made for printing, they are general enough to be used in other areas; i.e. display.
The selection of metrics must be based on the proporties of the attributes.
I.e. for sharpness the metrics should account for details and edges.
Pedersen, M.; Bonnier, N.; Hardeberg, J. Y. & Albregtsen, F. Attributes of Image Quality for Color Prints. Journal of Electronic Imaging, 2010, 19,

72. Evaluation of printer workflows
Using the quality attributes proposed by Pedersen et al. (2010)
Suitable metrics for each of the attributes were found.
Four different printers evaluated.
Details can be found in
Pedersen, M. Image quality metrics for the evaluation of printing workflows. University of Oslo, 2011
Color
Lightness
Sharpness
Contrast
Artifacts
Physical
Pedersen, M.; Bonnier, N.; Hardeberg, J. Y. & Albregtsen, F. Attributes of Image Quality for Color Prints Journal of Electronic Imaging, 2010, 19,

73. Calculate metrics for different attributes
Framework
Creating a digital version of the printed image.
Using the framework by Pedersen and Amirshahi.
Print the images
Scan
Perform registration
Calculate metrics for different attributes
Visualize results
Pedersen, M. & Amirshahi, S. A. Framework the evaluation of color prints using image quality metrics. 5th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), IS&T, 2010, 75-82

74. Sharpness Visualization of results are done with spider plots.
Pedersen, M. Image quality metrics for the evaluation of printing workflows. PhD thesis. University of Oslo, 2011

75. Noise
Pedersen, M. Image quality metrics for the evaluation of printing workflows. PhD thesis. University of Oslo, 2011

76. Color
Pedersen, M. Image quality metrics for the evaluation of printing workflows. PhD thesis. University of Oslo, 2011

77. Evaluation of projection systems
Similar to the printing evaluation, but using a camera instead of a scanner.
Ping Zhao, and Marius Pedersen. Measuring Perceived Sharpness of Projection Displays with A Calibrated Camera. Submitted.
Ping Zhao, Marius Pedersen, Jon Yngve Hardeberg, and Jean-Baptiste Thomas . Measuring the Relative Image Contrast Of Projection Displays. Journal of Imaging Science and Technology (JIST), Volume 59, Issue 3, Page , Society for Imaging Science and Technology, May, 2015.
Ping Zhao, Marius Pedersen, Jon Yngve Hardeberg, and Jean-Baptiste Thomas. Image Registration for Quality Assessment of Projection Displays. Published in Proceedings of 21st International Conference on Image Processing (ICIP 2014), Page , Paris, France, October, 2014.

78. Thank you for your attention
Contact information:
Marius Pedersen
Web:
Phone: (+47)
Mobile: (+47)