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Evaluation of Two Principal Image Quality Assessment Models

Evaluation of Two Principal Image Quality Assessment Models. Martin Č adík, Pavel Slav ík Czech Technical University in Prague, Czech Republic cadikm @sgi.felk.cvut.cz. Content. Image Quality Assessment Traditional error sensitivity approach, VDP Structure similarity approach, SSIM

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Evaluation of Two Principal Image Quality Assessment Models

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  1. 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. Content • Image Quality Assessment • Traditional error sensitivity approach, VDP • Structure similarity approach, SSIM • Traditional vs. Structural Approach • Conclusion

  3. 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. Image Quality Assessment Models • RMSE is NOT sufficient MODEL( , )= Detection probability map

  5. Image Quality Assessment & Computer Graphics • Quality improvement • Saving of resources • Effective visualization of information • etc.

  6. 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. Visible Differences Predictor • [Daly 93] • Threshold sensitivity • Visual Masking

  8. 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. Structural SIMilarity Index • [Wang 04] • Simple implementation • Fast computation

  10. 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. Traditional vs. Structural – Objective Testing Original (left) and ROI compressed (right) input images SSIM probability map (left) and VDP probability map (right)

  12. Traditional vs. Structural – Test Results Quality predictions compared to subjective MOS for the SSIM (left) and for the VDP (right)

  13. Traditional vs. Structural – Test Results (cont.) Quality assessment performances of the SSIM and for the VDP models CC – Pearson (parametric) correlation coefficient SROCC – Spearman (non-parametric) correlation coefficient

  14. 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. Thank You for Your Attention • ANY QUESTIONS?cadikm@sgi.felk.cvut.cz • ACKNOWLEDGEMENTSThis 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.

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