  In this paper, we analyze the effect of boosting in image quality assessment
through multi-method fusion. Existing multi-method studies focus on proposing a
single quality estimator. On the contrary, we investigate the generalizability
of multi-method fusion as a framework. In addition to support vector machines
that are commonly used in the multi-method fusion, we propose using neural
networks in the boosting. To span different types of image quality assessment
algorithms, we use quality estimators based on fidelity, perceptually-extended
fidelity, structural similarity, spectral similarity, color, and learning. In
the experiments, we perform k-fold cross validation using the LIVE, the
multiply distorted LIVE, and the TID 2013 databases and the performance of
image quality assessment algorithms are measured via accuracy-, linearity-, and
ranking-based metrics. Based on the experiments, we show that boosting methods
generally improve the performance of image quality assessment and the level of
improvement depends on the type of the boosting algorithm. Our experimental
results also indicate that boosting the worst performing quality estimator with
two or more additional methods leads to statistically significant performance
enhancements independent of the boosting technique and neural network-based
boosting outperforms support vector machine-based boosting when two or more
methods are fused.
