The potential use of vision-based algorithms for hiding defective display pixels is quite appealing. Two prior approaches
utilized either the point spread function (PSF) or contrast sensitivity functions to represent effects of the human visual
system. A third approach proposed in this paper includes a simple model of human visual masking characteristics
to improve theoretical defect hiding effectiveness. A visual experiment indicated all three methods provided significant
improvement over uncompensated sub-pixel defects across all color patches and images tested. The masking-based
method and an empirically optimized PSF method were more effective due to the masking-type patterns generated.
Hiding effectiveness was linearly related to the inverse of the lightness error generated by a defect. For moderate
lightness errors, both the PSF and masking-based methods completely hid the sub-pixel defects, with decreasing
effectiveness for larger lightness errors. Similar results were found for images and corresponding color patches, though
some dependency on the image content was observed for two of the five images. With the addition of a simple visual
masking effects model, the iCAM Image Difference Model was found to predict the general performance trends of the
three methods with reasonable accuracy.
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