Abstract
Stereoscopic Image becomes an attractive tool in image processing area. However, such as in 2D, this kind of images can be also affected by some types of degradations. In this paper, we are interesting by the impact of some of these degradation types on the perceived quality and we propose a new framework for Stereoscopic Image Quality Metric without reference (SNR-IQM) based on a degradation identification and features fusion steps. Support Vector Machine (SVM) models have been here used. The aptitude of our method to predict the subjective judgments has been evaluated using the 3D LIVE Image Quality Dataset and compared with some recent methods considered as the state-of-the-art. The obtained experimental results show the relevance of our work.
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Chetouani, A. (2015). Toward a Universal Stereoscopic Image Quality Metric Without Reference. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_52
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DOI: https://doi.org/10.1007/978-3-319-25903-1_52
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