  The quality assessment of edges in an image is an important topic as it helps
to benchmark the performance of edge detectors, and edge-aware filters that are
used in a wide range of image processing tasks. The most popular image quality
metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and
Structural similarity (SSIM) metrics for assessing and justifying the quality
of edges. However, they do not address the structural and functional accuracy
of edges in images with a wide range of natural variabilities. In this review,
we provide an overview of all the most relevant performance metrics that can be
used to benchmark the quality performance of edges in images. We identify four
major groups of metrics and also provide a critical insight into the evaluation
protocol and governing equations.
