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Proceedings Paper

Image quality assessment by preprocessing and full reference model combination
Author(s): S. Bianco; G. Ciocca; F. Marini; R. Schettini
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Paper 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.

Paper Details

Date Published: 19 January 2009
PDF: 9 pages
Proc. SPIE 7242, Image Quality and System Performance VI, 72420O (19 January 2009); doi: 10.1117/12.806693
Show Author Affiliations
S. Bianco, Univ. degli Studi di Milano-Bicocca (Italy)
G. Ciocca, Univ. degli Studi di Milano-Bicocca (Italy)
F. Marini, Univ. degli Studi di Milano-Bicocca (Italy)
R. Schettini, Univ. degli Studi di Milano-Bicocca (Italy)

Published in SPIE Proceedings Vol. 7242:
Image Quality and System Performance VI
Susan P. Farnand; Frans Gaykema, Editor(s)

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