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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

Evaluation of Two Principal Image Quality Assessment Models

Martin Čadík, Pavel Slavík

Czech Technical University in Prague, Czech Republic

[email protected]


Content
Content

  • Image Quality Assessment

  • Traditional error sensitivity approach, VDP

  • Structure similarity approach, SSIM

  • Traditional vs. Structural Approach

  • Conclusion


Image quality assessment
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


Image quality assessment models
Image Quality Assessment Models

  • RMSE is NOT sufficient

MODEL( , )=

Detection probability map


Image quality assessment computer graphics
Image Quality Assessment & Computer Graphics

  • Quality improvement

  • Saving of resources

  • Effective visualization of information

  • etc.


Error sensitivity based approach
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]


Visible differences predictor
Visible Differences Predictor

  • [Daly 93]

  • Threshold sensitivity

  • Visual Masking


Structural similarity based approach
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]


Structural similarity index
Structural SIMilarity Index

  • [Wang 04]

  • Simple implementation

  • Fast computation


Traditional vs structural subjective testing
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


Traditional vs structural objective testing
Traditional vs. Structural – Objective Testing

Original (left) and ROI compressed (right) input images

SSIM probability map (left) and

VDP probability map (right)


Traditional vs structural test results
Traditional vs. Structural – Test Results

Quality predictions compared to subjective MOS for the SSIM (left) and for the VDP (right)


Traditional vs structural test results cont
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


Conclusion
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


Thank you for your attention
Thank You for Your Attention

  • ANY [email protected]

  • 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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