A Log-Gabor Feature-Based Quality Assessment Model for Screen Content Images

In this paper, an image quality assessment (IQA) model for conducting objective evaluations of screen content images (S-CIs) is proposed, called the log-Gabor feature-based model (LGFM). From the standpoint of signal representation, the log-Gabor filters outperform the classical Gabor filters since the outputs of the log-Gabor filters are more consistent with the perception of visual cortex in human visual system (HVS). Furthermore, the following two remarkable characteristics of the log-Gabor filters are highly beneficial to develop a more accurate IQA model; i.e., (i) zero response at the DC, and (ii) stronger response at high frequencies. In our proposed L-GFM, the log-Gabor filters are used to extract features from the luminance of the reference SCIs and that of the distorted SCIs for measuring their degree of similarity. Together with the measurements from the other two chrominance components, the final LGFM score will be arrived at the output of the pooling stage. Extensive simulation results have shown that our proposed LGFM is highly consistent with the human perception, compared to other state-of-the-art IQA models.