Visually significant edges

Abstract

Numerous image processing and computer graphics methods make use of either explicitly computed strength of image edges, or an implicit edge strength definition that is integrated into their algorithms. In both cases, the end result is highly affected by the computation of edge strength. We address several shortcomings of the widely used gradient magnitude-based edge strength model through the computation of a hypothetical Human Visual System (HVS) response at edge locations. Contrary to gradient magnitude, the resulting “visual significance” values account for various HVS mechanisms such as luminance adaptation and visual masking, and are scaled in perceptually linear units that are uniform across images. The visual significance computation is implemented in a fast multiscale second-generation wavelet framework which we use to demonstrate the differences in image retargeting, HDR image stitching, and tone mapping applications with respect to the gradient magnitude model. Our results suggest that simple perceptual models provide qualitative improvements on applications utilizing edge strength at the cost of a modest computational burden.