Monogenic signal theory based feature similarity index for image quality assessment

Abstract

Image quality assessment (IQA) aims to establish generic metrics consistently with subjective evaluations using computational models. Recent phase congruency, which is a dimensionless, normalized feature of a local structure, is used as the structure similarity feature. This paper proposes a novel feature similarity index that is Riesz transform based monogenic phase congruency feature similarity index (RMFSIM). RMFSIM is based on monogenic signal theory for full reference IQA. Monogenic phase congruency (MPC) map, instead of phase congruency map, is constructed utilizing the local phase, the local orientation and the local energy information of the 2D monogenic signal. The corresponding 1st-order and 2nd-order coefficients of the MPC map are obtained by Riesz transform. The local feature coefficients similarities are computed by the similarity measure, and then a final single quality score is derived from weighting feature coefficients similarities. Experimental results demonstrate that the proposed similarity index is highly consistent with human subjective evaluations and achieves good performance with the existing state-of-the-art methods in terms of prediction monotonicity and accuracy.