Image quality assessment by preprocessing and full reference model combination

S. Bianco; G. Ciocca; F. Marini; R. Schettini

doi:10.1117/12.806693

19 January 2009 Image quality assessment by preprocessing and full reference model combination

S. Bianco, G. Ciocca, F. Marini, R. Schettini

Proceedings Volume 7242, Image Quality and System Performance VI; 72420O (2009) https://doi.org/10.1117/12.806693
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

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.

Citation Download Citation

S. Bianco, G. Ciocca, F. Marini, and R. Schettini "Image quality assessment by preprocessing and full reference model combination", Proc. SPIE 7242, Image Quality and System Performance VI, 72420O (19 January 2009); https://doi.org/10.1117/12.806693

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