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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

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Importance of region-of-interest on image difference metrics

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  1. 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

  2. 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

  3. 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.

  4. 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?

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Questions? Thanks for your attention.

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