Fusion of imprecise data applied to Image Quality Assessment
Résumé
The estimation of dependence relationships between variables is generally performed using probabilistic models. However, these models are not adapted to imprecise data and they cannot easily take into account symbolic information such as experts opinions. On the contrary, evidence theory also called theory of belief function, allow to integrate these kinds of uncertainties. In this paper we propose regression analysis based on a fuzzy extension of belief function theory, applied to image quality assessment problem. For a given input vector x of relevant images feature, the method provides a prediction regarding the value of the output variable y which represents the score of subjective image quality test, namely the DMOS value. To validate the proposed approach, experiments are conducted on LIVE image database. The proposed measure is compared with algorithms based on general regression as neural networks and Support Vector Machine (SVM). The framework of this paper is of nature subjective and results show that our approach performs well and illustrate the interest of the theory of belief function in this context.