Monogenic Signal Theory Based Feature Similarity Index For Image Quality Assessment

-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 (RMFSIM) index for full reference IQA based on monogenic signal theory. A monogenic phase congruency map, which is equipped to be relatively insensitive to noise variations, is constructed using phase, orientation and 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 similarity is computed by the similarity measure and a single similarity score are combined together finally. Experimental results demonstrate that the proposed similarity index is highly consistent with human subjective evaluations and achieves good performance in terms of prediction monotonicity and accuracy. Key-Words: image quality assessment(IQA); monogenic phase congruency(MPC); human visual system; Feature Similarity Index