color image quality assessment part ii: image quality metrics marius pedersen the norwegian colour...

Color Image Quality Assessment Part II:

Image Quality MetricsMarius Pedersen

The Norwegian Colour and Visual Computing Laboratory, Gjøvik University College, Gjøvik, Norway

[email protected] P. Allebach

School of ECE, Purdue UniversityWest Lafayette, [email protected]

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mailto:[email protected]


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.

Image Metric Measure of quality

4

Different types of metrics• Three main types of metrics:

– Full-reference.– No-reference.– Reduced-reference.

Reproduction

Original

MetricMeasure of quality

5

Existing image quality metrics• Metrics usually follow a common framework.• Different stages:

• Unless stated otherwise we focus on full-reference

Color space transforms

Human visual system models

Quality calculation Pooling

Fewer

Many

Quality value

Original and reproduction

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.

Quality calculationA

B

Distance = quality

25/09/12 Eq. from http://en.wikipedia.org/wiki/Euclidean_distance

http://en.wikipedia.org/wiki/Euclidean_distance

9

Pooling• 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.

Pooling

Quality map Pooling

Fewer

Many

11

Classification of metrics• Metrics can be classified into

several categories:– Mathematically based

metrics.• MSE or

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

– 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.


13

Classification of metrics• To understand the metrics we

propose a classification of them into:– Mathematically based metrics.

• MSE or


– 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.


14

Classification of metrics• To understand the metrics we

propose a classification of them into:– Mathematically based metrics.

• MSE or


– 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.


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: • 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.

• has served as a satisfactory tool for measuring perceptual difference between uniform color patches

17

Mathematically based metrics: • 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 752909–1–10, San Jose, CA, USA, Jan 2010. SPIE.

19

Example – image difference maps

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.

Figure from C. A. Bouman: Digital Image Processing - January 9, 2012 (25/09/12: https://engineering.purdue.edu/~bouman/ece637/notes/pdf/Opponent.pdf)

https://engineering.purdue.edu/~bouman/ece637/notes/pdf/Opponent.pdf

https://engineering.purdue.edu/~bouman/ece637/notes/pdf/Opponent.pdf

23

Low-level based metrics: S-CIELAB• was not correlated with perceived image

difference. • Zhang and Wandell proposed a spatial extension

based on • 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, 731-734

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– to calculate color differences.

• Pooling: – Usually taking the average.

Figure from http://white.stanford.edu/~brian/scielab/scielab3/scielab3.pdf 14/09/12

http://white.stanford.edu/~brian/scielab/scielab3/scielab3.pdf

http://white.stanford.edu/~brian/scielab/scielab3/scielab3.pdf

25

S-CIELAB CSFs

Figure from Johnson, G. M. & Fairchild, M. D. Darwinism of Color Image Difference Models. Color Imaging Conference, 2001, 108-112

26

Example S-CIELAB• Want to test S-CIELAB? Matlab code available online at

http://white.stanford.edu/~brian/scielab/scielab.html• 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

http://white.stanford.edu/~brian/scielab/scielab.html

27

Example S-CIELAB - maps

20 40 60 80 100 120 140 160 180

20

40

60

80

100

1205

10

15

20

25

30

35

40

45

50

18 inches viewing distanceMean=3.4, Min=0.4, Max=52.6, median=2.4

20 40 60 80 100 120 140 160 180

20

40

60

80

100

120

5

10

15

20

25

44.5 inches viewing distanceMean=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 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, 023021-1-023021-10

Comparison of filtering methods• Different filtering methods: CSFs (S-CIELAB), bilateral

filter (from ABF), CSFs in NSCT (Pedersen et al.).

Original29

S-CIELAB NSCTABF

Pedersen, M.; Liu, X. & Farup, I.. Improved Simulation of Image Detail Visibility using the Non-Subsampled Contourlet Transform. Color and Imaging Conference, 2013

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, 600-612

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, and are image content in local window j, and W indicates the total number of local windows.

35

Example SSIM• Want to test SSIM?

https://ece.uwaterloo.ca/~z70wang/research/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 = [0.05 0.05];– window = ones(8); (window size)– L = 100; (dynamic range)

https://ece.uwaterloo.ca/~z70wang/research/ssim/

https://ece.uwaterloo.ca/~z70wang/research/ssim/

36

Example SSIM - maps

20 40 60 80 100 120 140 160 180

10

20

30

40

50

60

70

80

90

100

110 0.3

0.4

0.5

0.6

0.7

0.8

0.9

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, 2284-2298J. Lee and T. Horiuchi. Image quality assessment for color halftone images based on color structural similarity. IEICE Trans. Fundamentals, E91A:1392–1399, 2008.

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: http://scien.stanford.edu/pages/labsite/1998/psych221/projects/98/dctune/yuke/page2.htm

http://scien.stanford.edu/pages/labsite/1998/psych221/projects/98/dctune/yuke/page2.htm

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.

Beach image 17/09/15: https://foundationsofvision.stanford.edu/chapter-7-pattern-sensitivity/

https://foundationsofvision.stanford.edu/chapter-7-pattern-sensitivity/

https://foundationsofvision.stanford.edu/chapter-7-pattern-sensitivity/

43

Pooling – one step further• Pooling is very important for achieving an IQ

metric correlated with the percept.

Pooling

Quality map

Pooling

Fewer

Many

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 2006. 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

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 2006. 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 724012, 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, 685-696.

• 25/09/12: http://www.sciencedirect.com/science/article/pii/S1047320312000600

• The overall results indicate that:– Pooling parameters are important.– Pooling is metric dependent.

http://www.sciencedirect.com/science/article/pii/S1047320312000600




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: 3-901-906-26-6, 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 experimentReference Test set

50 30 7050

30

Trial 1

Categories: 1-77040 50 60

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

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.

bReproduction Original

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. 3440-3451, 2006“Image from http://sse.tongji.edu.cn/linzhang/IQA/IQA.htm (04/07/13)

http://sse.tongji.edu.cn/linzhang/IQA/IQA.htm

http://sse.tongji.edu.cn/linzhang/IQA/IQA.htm

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, 120-125

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

Thanks to Xinwei Liu for putting together the table.

Name CID:IQ

TID

LIVE (Release 2)

Toyama

CPIQ IRCCyN/IVC

VCL@FER VAIQ

TUD

JPEGXR HTI IBBI MMS

P 3D A57 WIQTID2013

TID2008 CSIQ DRIQ IVC

Watermarking

3D image

Art image TUD1 TUD2

EnricoBroke

n Arrow

s

Fourier

Subband

Meerwald

Year 2014 2013 2008 2006 2008 2010 2012 2005 2007 2009 2009 2009 2008 2009 2011 2009 2010 2010 2011 2011 2011 2010 2007 2009

Color or Gray Color Color Color Color Color Color Color Color Gray Gray Gray Gray Color Color Color Color Color Color Color Color Color Color Gray Gray

Number of reference

image 23 25 25 29 14 30 26 10 5 10 5 12 6 8 23 42 8 11 10 12 12 9 3 7Number of

distortion type 6 24 17 5 5 6 3 5 10 2 6 2 15 3 4 1 1 1 1 1 6 1

Number of distortion level 5 5 4 X 6 5 5 2 6 7 5 1 5 6 2 4 6 5 5 3 X

Number of image 690 3000 1725 808 196 896 104 195 105 130 315 132 96 120 575 42 16 55 60 60 60 60 54 80

Number of observer 17 985 838 29 16 35 9 15 16 17 7 14

No Specif

y 20 118 15 12 20

No Specif

y 18 18 20 7 30

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

Dugay

PedersenSimoneAjagamelleIVC


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.

∆E*ab SHAME S-CIELAB PSNR SSIM VSNR-0.15

0.0499999999999999

0.25

0.45

0.65

0.85

IVC database, Le Callet et al. Gamut mapped images, Dugay et al.Luminance changed images, Pedersen et al. JPEG and JPEG2000 compressed images, Caracciolo et al.Images altered in contrast, lightness, and saturation, Ajagamelle et al. TID2008, Ponomarenko et al.

Pear

son

corr

elati

on

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 2015. 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.

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, 011016-1-13

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

SharpnessContrast

Artifacts

Physical

Pedersen, M.; Bonnier, N.; Hardeberg, J. Y. & Albregtsen, F. Attributes of Image Quality for Color Prints Journal of Electronic Imaging, 2010, 19, 011016-1-13

73

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


76

Color


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 030404-1-030404-13, 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 3488-3492, Paris, France, October, 2014.

Thank you for your attention

Contact information:Marius PedersenE-mail: [email protected] Web: www.colourlab.no Phone: (+47) 61 13 52 46Mobile: (+47) 93 63 43 85


http://www.colourlab.no/

color image quality assessment part ii: image quality metrics marius pedersen the norwegian colour...

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