Image Processing Laboratory DEEI, University of Trieste, Italy units.it/ipl ipl@units.it

1. Image Processing Laboratory DEEI, University of Trieste, Italy www.units.it/ipl ipl@units.it

2. Staff

3. Research (1) Dual Layer Display for Medical Applications Film-based radiographic image on a light box: 0.5 - 3000 cd/m² Medical-grade LCD display: 1 - 500 cd/m²  Dual LCD display prototype yields: 0.1 - 600 cd/m², pseudo-16-bit (cooperation with FIMI – Barco)

4. Research (2) High-Dynamic-Range Image Display Easy to acquire... ...difficult to display  Automatic space-variant luminance mapping (industrial appl.: welding)

5. Research (3) Forensic Image Processing • Analysis of (latent) fingerprints using synchrotron light • Shoeprints found on the crime scene: automatic identification of the make and model of the shoe that left the mark • Image processing algorithms and software to be used in courtrooms (with a start-up company, Amped)

6. Research (4) No-Reference Video Quality Assessment • Nonuniform-grid blockiness • Blurriness (cooperation with Philips Consumer Electronics)

7. Research (5) Digital Restoration of Antique Documents • Ancient books • Photographic Prints • Glass photographic negatives • Film and Videotapes

8. Research (6) Advanced instrumentation for applied physics experiments Electronics for pump-and-probe experiments Asymmetrical cantilevers for single molecules detection

9. Current Projects: • Forensic imaging with synchrotron light (Fondo Trieste, 2009-10) • CHIRON (health management) (EU Artemis JU, 2010-13) • ELADIN 2 (high dynamic range imaging) (FVG Region, 2009-10) • Image quality metrics (Philips Electronics Nederland B.V., 2008-10)

10. Contacts: • Image Processing Laboratory, DEEI, University of Trieste, Trieste, Italy • http://www.units.it/ipl • email: ipl@units.it

12. Blurriness metric • Frame blurriness estimation • Objective artefacts analysis: • blurriness measurements • no-reference blurred edges localization • Measures based on HVS models: • Visual Attention • Image Clutter

13. Blurred edge localization • Image divided in blocks and morphological gradient before and after anisotropic diffusion (MGR) • Gradient values in range [ mean(Igm’), mean(Igm’)+∆ ] indicate blurring • Percentage of block edges satisfying previous condition (DEP) • Estimation of detail loss in the single block is the estimation index BE=MGR/DEP

14. Perceptual model • Visual Attention Model by Koch and Ullman • Visual Clutter related to the average time to detect a blurred object, segmentation algorithm proposed by Felzenswalb • DEP evaluated only on spots of attention • blurriness annoyance is related to the clutter amount

15. Detail loss for different quality levels iPod, 1P-Intermediate, CE-Baseline, CQ-ASP and SA-Blu-Ray.

16. MGR for improving coding quality

17. Localization of blurred edges

18. Blocks for high BE values

19. Blocks for low BE values

20. Blocks with small number of regions and different DEP

21. Blocks with same DEP and different number of regions

22. Blockiness metric Detection in smooth object • Picture is scanned in groups of rows with overlapping. Rows are split in sections, in order to have the method work locally. • For each group of rows, and each section, the points of local maxima of differences are found and averages are used as estimation of the blockiness inside smooth object parts. • Discrimination is performed via a threshold.

23. Detection on object edges High-activity areas  high magnitude of the image gradient, (Sobel + some morphological operations) • Long straight edge  heavier blockiness. More visible and annoying. • Both sides of a straight edge are smooth  coarse quantization  the straight edge is caused by blockiness. • Search for squared corners in smooth areas

24. Results original frame

25. Results compressed frame

26. Results detail in the original and in the compressed frames

27. Results

28. Conclusions • Unify the blurriness and blockiness estimated parameters in a single quality index • Adapt the proposed quantification criteria for blockiness to the actual subjective annoyance of the blocking artefact • Subjective tests will be conducted in order to validate the proposed objective no-reference metric

29. Detail loss • Main context selection: anisotropic diffusion Ad (I)->I’ • Cancellation of short, smooth edges • Preservation of long, sharp edges • Activity measure: morphological gradient Igm(i , j) = maxM(i , j) - minM(i , j) • M(i , j) = I (u, v)| i-1 < u < i + 1, j -1 < v < j + 1 • Index of preserved detail MGR = mean(Igm)/mean(Igm’) • High MGR -> high amount of detail -> Well preserved picture

30. Blockiness metric Detection in smooth objects (e.g. across columns) where

31. Detection in smooth object • Picture is scanned in groups of 4 rows with overlapping. Rows are split in sections, in order to have the method work locally. • For each group of rows, and each section, the points of local maxima of the difference d_i (n) are found, and indices r_i (n) and ϕ_i (n) are computed in these points. • Discrimination is performed via a threshold.

32. Blockiness quantification These averages are used as estimation of the blockiness inside smooth object parts.