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Evaluation of Image Quality Assessment Models: SSIM vs. VDP

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madaline-berger
15 diapositives · 303 Views · Nov 14, 2014

This study evaluates two principal image quality assessment (IQA) models: the Traditional Error Sensitivity Approach (VDP) and the Structural Similarity Approach (SSIM). By conducting subjective and objective tests, we compare their effectiveness in assessing image quality, particularly in terms of compression and visualization. Results indicate that SSIM performs better, is faster to compute, and is simpler to implement than VDP. However, both models struggle with region-of-interest tasks. This paper highlights the significant advantages of SSIM as an alternative to the thoroughly verified VDP model.

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Evaluation of Image Quality Assessment Models: SSIM vs. VDP
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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.