Toward a universal learning-based image quality metric with reference for stereoscopic images

A Full Reference Image Quality Metric (FR-IQM) for stereoscopic images based on two main steps is presented in this paper. The first step consists to select some features according to the considered degradation types and combine those features using an Artificial Neural Network (ANN). These features are here extracted from a Cyclopean Image (CI). At the end of this step, a FR-IQM per degradation is thus obtained. In order to provide a single objective score that is in accordance with the subjective judgment, outputs of all ANN models are combined in a second step. The efficiency of our method has been evaluated using the 3D LIVE Image Quality Database that is composed of 5 types of degradation with 365 degraded images. Its performance is discussed and compared to some recent metrics.