Quality estimation based on interest points through hierarchical saliency maps
Quality of Experience (QoE) is a widely used notion nowadays because the end-user has been re-integrated in the quality loop. Subjective experiments are tedious and time consuming but to date they are the main way to have the human judgment. An important effort has been put on the development of metric estimating the QoE. So, in this paper, we propose a new image quality metric based on two concepts: the interest points and the objects saliency on color images. This metric is constructed by taking advantage of the variability of the interest points impact function of the image impairment and also the hierarchical attention of a human observer. This combination helps in giving more importance to the variability of the most salient regions and reducing the influence of regions having less visual importance. The results show a high correlation between the metric scores and the human judgment and a better quality range than well-known metrics.