Recent image quality assessment (IQA) metrics achieve high correlation with human perception of image quality. Naturally, it is of interest to produce even better results. One promising method is to weight image quality measurements by visual importance. To this end, we describe three strategies - visual fixation-based weighting, quality-based weighting and weighting based on distribution of local quality scores about the mean. By contrast with some prior studies we find that these strategies can improve the correlations with subjective judgment significantly. We demonstrate improvements on the SSIM index in both its multi-scale and single-scale versions, using the LIVE database as a test-bed. / text