Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review

TL;DR

This paper reviews various image quality metrics for evaluating edges and edge-aware filters, highlighting their strengths and limitations in capturing structural and functional accuracy across diverse natural images.

Contribution

It provides a comprehensive overview of key edge quality assessment metrics, categorizes them into four groups, and offers critical insights into evaluation protocols and underlying equations.

Findings

Identifies four major groups of edge quality metrics

Highlights limitations of common metrics like MSE, PSNR, SSIM

Provides guidelines for benchmarking edge detection performance

Abstract

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.