Saliency-based deep blind image quality assessment

Assessing the quality of images is a challenging task. To achieve this goal, the images must be evaluated by a pool of subjects following a well-defined assessment protocol or an objective quality metric must be defined. In this contribution, an objective metric based on neural networks is proposed. The model takes into account the human vision system by computing a saliency map of the image under test. The system is based on two modules: the first one is trained using normalized distorted images. It learns the features from the original and the distorted images and the estimated saliency map. Furthermore, an estimate of the prediction error is performed. The second module (non-linear regression module) is trained with the available subjective scores. The performances of the proposed metric have been evaluated by using state of the art quality assessment datasets. The achieved results show the effectiveness of the proposed system in matching the subjective quality score.