We investigate the impact of overt visual attention and perceived interest on the prediction performance of image quality metrics. Towards this end we performed two respective experiments to capture these mechanisms: an eye gaze tracking experiment and a region-of-interest selection experiment. Perceptual relevance maps were created from both experiments and integrated into the design of the image quality metrics. Correlation analysis shows that indeed there is an added value of integrating these perceptual relevance maps. We reveal that the improvement in prediction accuracy is not statistically different between fixation density maps from eye gaze tracking data and region-of-interest maps, thus, indicating the robustness of different perceptual relevance maps for the performance gain of image quality metrics. Interestingly, however, we found that thresholding of region-of-interest maps into binary maps significantly deteriorates prediction performance gain for image quality metrics. We provide a detailed analysis and discussion of the results as well as the conceptual and methodological differences between capturing overt visual attention and perceived interest.