Blind quality estimator for 3D images based on binocular combination and extreme learning machine

Abstract Over recent years, blind quality estimators for three-dimensional (3D) images have received increasing attention. In this paper, we describe a blind quality estimator for 3D images based on binocular combination and an extreme learning machine (ELM) for more accurate alignment with subjective human experience. First, two binocular combinations of stimuli are generated using different combination strategies. Various binocular quality-aware features of these combinations are then extracted by local binary pattern operators, which give an effective description of the degradation pattern. Finally, these features are mapped to the subjective quality score of the distorted 3D image using an ELM. Experimental results using three benchmark databases confirm that the proposed metric is effective and achieves competitive prediction performance when compared with most current full reference and blind metrics.