Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review
It addresses the need for better quality assessment in image processing for researchers and practitioners, but is incremental as it reviews existing metrics rather than proposing new ones.
This review tackles the problem of assessing edge quality in images by providing an overview of performance metrics for benchmarking edge detectors and filters, identifying four major groups of metrics and offering critical insights into evaluation protocols.
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.