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

arXiv: https://arxiv.org/abs/1801.00454 · HTML: https://mlsanity.com/paper/1801.00454/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [Tiny and Efficient Model for the Edge Detection Generalization](https://mlsanity.com/paper/2308.06468/) (2023)
- [The Effectiveness of Edge Detection Evaluation Metrics for Automated Coastline Detection](https://mlsanity.com/paper/2405.11498/) (2024)
- [Modeling Image Quantization Tradeoffs for Optimal Compression](https://mlsanity.com/paper/2112.07207/) (2021)
- [High-Quality Tomographic Image Reconstruction Integrating Neural Networks and Mathematical Optimization](https://mlsanity.com/paper/2509.06082/) (2025)

## Abstract (arXiv)

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.