A universal Full Reference image Quality Metric based on a neural fusion approach

Abstract

We present in this paper a new global Full-Reference (FR) image quality metric (IQM) based on the fusion of several conventional FR metrics using an ANN learning algorithm. The fusion is shown to result in improved performance compared to individual FR metrics. Indeed, existing FR metrics can provide excellent results for specific degradations but poor results for others. Here, we propose to overcome this limitation by first improving the performance of existing FR metrics across different degradations through a ranking process. Then, using an Artificial Neural Network, we fuse the best-performing measures into a single metric called Global Index Quality Metric (G-IQM). The experimental results using the TID 2008 image database demonstrate that this new G-IQM metric achieves consistent image quality evaluation results with subjective evaluation.