Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.
Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics
