Image Quality Assessment Based on Intrinsic Mode Function Coefficients Modeling

Reduced reference image quality assessment (RRIQA) methods aim to assess the quality of a perceived image with only a reduced cue from its original version, called ”reference image”. The powerful advantage of RR methods is their ”General-purpose”. However, most introduced RR methods are built upon a non-adaptive transform models. This can limit the scope of RR methods to a small number of distortion types. In this work, we propose a bi-dimensional empirical mode decomposition-based RRIQA method. First, we decompose both, reference and distorted images, into Intrinsic Mode Functions (IMF), then we use the Generalized Gaussian Density (GGD) to model IMF coefficients. Finally, the distortion measure is computed from the ”fitting errors”, between the empirical and the theoretical IMF histograms, using the Kullback Leibler Divergence (KLD). In order to evaluate the performance of the proposed method, two approaches have been investigated : the logistic function-based regression and the well known Support vector machine-based classification. Experimental results show a high correlation between objective and subjective scores.