This paper addresses the problem of assessing full-reference visual quality of images. A correlation between the obtained array of mean opinion scores (MOS) and the corresponding array of given metric values allows characterizing a correspondence of the considered metric to HVS. For the database TID2013 intended for a metric verification, a Spearman correlation is about 0.85 for the best existing HVS-metrics. A simple way to improve an efficiency of assessing visual quality of images is to combine several metrics: as a product of two existing metrics in certain powers that can be optimized or applying more complex structures to unify more than two visual quality metrics. We show that clustering methods can be efficiently used for this purpose. This method provides essentially larger improvement of a combined metric performance compared to the method based on their multiplication. Besides, our work specially addresses assessing images with multiple distortions. There are two such types in the modified LIVE database and two others in TID2013. Spearman rank order correlation coefficient (SROCC) between a combined metric and mean opinion score for a considered database serves as a criterion for the metric optimization. As the result of our design, the SROCC reaches 0.95 for the verification set of the database TID2013. This is considerably better than for any particular metric employed as an input where FSIMc is the best among them.