1. department of computer science and engineering Evaluation of Two Principal Image Quality Assessment Models Martin Čadík, Pavel Slavík Czech Technical University in Prague, Czech Republic cadikm@sgi.felk.cvut.cz

2. TU Wien, November 5, 2004 (2) department of computer science and engineering Content Image Quality Assessment Traditional error sensitivity approach, VDP Structure similarity approach, SSIM Traditional vs. Structural Approach Conclusion

3. TU Wien, November 5, 2004 (3) department of computer science and engineering Image Quality Assessment Assessing the quality of images –image compression –transmission of images Subjective testing –the proper solution –expensive –time demanding –impossible embedding into algorithms

4. TU Wien, November 5, 2004 (4) department of computer science and engineering Image Quality Assessment Models RMSE is NOT sufficient MODEL (, )= Detection probability map

5. TU Wien, November 5, 2004 (5) department of computer science and engineering Image Quality Assessment & Computer Graphics Quality improvement Saving of resources Effective visualization of information etc.

6. TU Wien, November 5, 2004 (6) department of computer science and engineering Error Sensitivity Based Approach General framework Visible Differences Predictor [Daly93] Perceptual Distortion Measure [Teo, Heeger 94] Visual Discrimination Model [Lubin 95] Gabor pyramid model [Taylor et al. 97] WVDP [Bradley 99]

7. TU Wien, November 5, 2004 (7) department of computer science and engineering Visible Differences Predictor [Daly 93] Threshold sensitivity Visual Masking

8. TU Wien, November 5, 2004 (8) department of computer science and engineering Structural Similarity Based Approach Main function of the HVS: to extract structural information UQI [Wang 02] SSIM [Wang 04] Multidimensional Quality Measure Using SVD [Shnayderman 04]

9. TU Wien, November 5, 2004 (9) department of computer science and engineering Structural SIMilarity Index [Wang 04] Simple implementation Fast computation

10. TU Wien, November 5, 2004 (10) department of computer science and engineering Traditional vs. Structural – Subjective Testing Independent subjective tests –32 subjects –30 uniformly compressed images (JPEG2000) –30 ROI compressed images –difference expressed by ratings Mean Opinion Scores

11. TU Wien, November 5, 2004 (11) department of computer science and engineering Traditional vs. Structural – Objective Testing Original (left) and ROI compressed (right) input images SSIM probability map (left) and VDP probability map (right)

12. TU Wien, November 5, 2004 (12) department of computer science and engineering Traditional vs. Structural – Test Results Quality predictions compared to subjective MOS for the SSIM (left) and for the VDP (right)

13. TU Wien, November 5, 2004 (13) department of computer science and engineering Traditional vs. Structural – Test Results (cont.) ModelCCCC-ROISROCC- ROI VDP0.220.440.39 SSIM0.720.510.45 Quality assessment performances of the SSIM and for the VDP models CC – Pearson (parametric) correlation coefficient SROCC – Spearman (non-parametric) correlation coefficient

14. TU Wien, November 5, 2004 (14) department of computer science and engineering Conclusion Independent comparison of two IQA approaches –VDP, SSIM –subjective data (uniform/ROI) Results –SSIM better –SSIM faster to compute and easier to implement –both models perform badly in ROI tasks –SSIM can detect the ROI => SSIM significant alternative to thoroughly verified VDP

15. TU Wien, November 5, 2004 (15) department of computer science and engineering Thank You for Your Attention ANY QUESTIONS? cadikm@sgi.felk.cvut.cz ACKNOWLEDGEMENTS This project has been partly supported by the Ministry of Education, Youth and Sports of the Czech Republic under research program No. Y04/98: 212300014, and by the CTU in Prague - grant No. CTU0408813. Thanks to Radek Vaclavik and Martin Klima for their support during the subjective testing.