Determining the perceived quality of a digital image can be done by showing it to a large group of people and ask them to rate its quality. However, this method is too cumbersome and time-consuming for most applications. Therefore, automatic metrics have been developed, which are able to objectively predict the perceived image quality without the need for any human input. Such image quality assessment metrics average the predicted quality across a whole image into a single quality value. To improve their performance, their predicted image quality is weighted with visual attention information: regions in the image that receive more attention are weighted more heavily in the quality assessment. Furthermore, the effect of a quality assessment task on visual attention is investigated via a large scale subjective experiment. The main findings are that 1) people who are looking freely pay more attention to the region of interest than people who are scoring the image quality, 2) applying the visual attention of people who are looking freely to image quality assessment metrics yields a higher performance gain than the visual attention of people who are scoring the image quality, and 3) the predicted image quality in the region of interest has a more positive influence on the overall predicted image quality than the quality in the background. In short, visual attention information can be used to increase the performance of image quality assessment metrics.