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Importance of region-of-interest on image difference metrics. Marius Pedersen The Norwegian Color Research Laboratory Faculty of Computer Science and Media Technology Gjøvik University College, Gjøvik, Norway

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importance of region of interest on image difference metrics

Importance of region-of-interest on image difference metrics

Marius Pedersen

The Norwegian Color Research Laboratory

Faculty of Computer Science and Media Technology

Gjøvik University College, Gjøvik, Norway

Marius.pedersen@hig.no http://www.colorlab.noSupervisors: Jon Yngve Hardeberg and Peter Nussbaum

Thesis presentation, 7. June 2007, Gjøvik

outline
Outline
  • Background
  • Research questions
  • Experimental setup
    • Psychophysical experiment
    • Image difference metrics
    • Region-of-interest
    • Images
    • Workflow
  • Results
    • Questionnaire results
    • How do we look at images?
    • Image difference metrics
  • Conclusion
background
Background
  • When we print an image we want the output to be as close to the original as possible.
  • How perceivable are changes made to an image by the observers?
  • Image difference metrics have been developed to answer this question, their goal is to predict the perceived image difference.
  • The image difference metrics used today do not predict the perceived image difference very well.
  • When observers view an image some regions are more important than others.
research questions
Research questions
  • Question 1: - Can region-of-interest improve overall image difference metrics in complex images?
  • Question 2: - How do observers look at images given different tasks?
the experiment
The experiment
  • A psychophysical experiment using 4 different scenes was carried out with 25 observers.
  • Using an eye tracker to record the gaze position of observers.
  • 4 different image difference metrics- ΔE*ab- S-CIELAB- SSIM- iCAM
  • Different region-of-interest- Freeview- Psychophysical experiment- Gaze marking - Observer marked
images
Images
  • Changes to images made only in lightness.
  • 4 global changes and 4 local changes.
  • 3 and 5 ΔE*ab globally and 3 ΔE*ab locally.
experiment workflow
Experiment workflow
  • Freeview task- Observers were told to look freely at the images.
  • Psychophysical experiment- Choose the image most similar to the original in a pair comparison task.
  • Gaze marking- Look at the regions important for your decision in the experiment.
  • Observer marking- Observers marked important regions on paper with a pen.
  • Questionnaire
questionnaire results
Questionnaire results
  • 25 observers ranging from 20 to 38 years, with a mean age of 24.
  • Recruited from the school
  • 56% experts and 44% non-experts.
  • 24% had participated in psychophysical experiments.
psychophysical experiment results
Psychophysical experiment results
  • Small global changes are rated better than higher global changes.
  • Overall results show that regions are rated generally better than global changes
  • Highly visible changes in small regions are given a low score.
how do we look at images
How do we look at images
  • Difference between experts and non-experts when it comes to marking important areas. - Expert mark smaller and more precise areas.
  • Same observations made with observers with psychophysical experience.
  • Experts use longer time to evaluate difference.
how do we look at images1
How do we look at images
  • Region-of-interest change when observer are given different tasks.
  • 2-D correlation coefficient used as a measure of similarity between groups and maps.

Freeview Psychophysical experiment Gaze marking Observer marking

image difference metrics results
Image difference metrics results
  • In the normal computation S-CIELAB, ΔE*aband the hue angle algorithm outperform SSIM and iCAM.
  • Pearson product-moment correlation coefficient used a measure of performance.
  • Scene 3 has a small but highly visible region, all metrics have problems here.
area based image difference
Area based image difference
  • In metrics performing well only a minor improvement is found.
  • While in metrics with a lower performance a bigger improvement is found.
  • Also the mean squared difference from the regression line supports the finding of improvement in the low performing metrics.
conclusion
Conclusion
  • Q1: Can region-of-interest improve overall image difference metrics in complex images?

- Region-of-interest can improve overall image difference metrics, especially in metrics with a low performance.

  • Q2: How do observers look at images given different tasks?

- Observer have different region-of-interest in different tasks.* In a freeview task semantic regions as faces draw attention* In a pair comparison task attention is drawn toward other areas where the observer locates difference but faces still draw attention.* Gaze marking cannot replace region-of-interest marking by hand. * Manual marking only reflects some areas of the gaze.

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Questions?

Thanks for your attention.