Full reference image quality metric for stereo images based on Cyclopean image computation and neural fusion

In this paper, we present a New Stereo Full-Reference Image Quality Metric (SFR-IQM) based on Cyclopean Image (CI) computation and 2D IQM fusion. The Cyclopean images of the reference image and its degraded version are first computed from the left and the right views. 2D measures are then extracted from the obtained CIs and are combined using an Artificial Neural Networks (ANN) in order to derive a single index. The 3D LIVE Image Quality Database has been here used to evaluate our method and its capability to predict the subjective judgments. The obtained results have been compared to some recent methods considered as the state-of-the-art. The experimental results show the relevance of our method.