This paper focuses on full-reference image quality assessment and presents different computational strategies aimed to improve the robustness and accuracy of some well known and widely used state of the art models, namely the Structural Similarity approach (SSIM) by Wang and Bovik and the S-CIELAB spatial-color model by Zhang and Wandell. We investigate the hypothesis that combining error images with a visual attention model could allow a better fit of the psycho-visual data of the LIVE Image Quality assessment Database Release 2. We show that the proposed quality assessment metric better correlates with the experimental data. © 2009 SPIE-IS&T.
Schettini, R., Marini, F., Ciocca, G., Bianco, S. (2009). Image Quality Assessment by Preprocessing and Full Reference Model Combination. In Image Quality and System Performance VI, IS&T/SPIE Symposium on Electronic Imaging (pp.72420O) [10.1117/12.806693].
Image Quality Assessment by Preprocessing and Full Reference Model Combination
SCHETTINI, RAIMONDO;CIOCCA, GIANLUIGI;BIANCO, SIMONE
2009
Abstract
This paper focuses on full-reference image quality assessment and presents different computational strategies aimed to improve the robustness and accuracy of some well known and widely used state of the art models, namely the Structural Similarity approach (SSIM) by Wang and Bovik and the S-CIELAB spatial-color model by Zhang and Wandell. We investigate the hypothesis that combining error images with a visual attention model could allow a better fit of the psycho-visual data of the LIVE Image Quality assessment Database Release 2. We show that the proposed quality assessment metric better correlates with the experimental data. © 2009 SPIE-IS&T.
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