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Recent image quality assessment (IQA) metrics achieve high correla- tion 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 strate- gies - 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.

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Anush Krishna Moorthy received his B.E. from the University of Pune, India, in 2007 and his M.S. from The University of Texas at Austin, USA in 2009. He is currently pursuing his PhD in Electrical Engineering at The University of Texas at Austin. He is the Assistant Director of the Laboratory for Image and Video Engineering (LIVE) at UT.

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