Just-Noticeable-Difference Based Edge Map Quality Measure
This work addresses the need for automated edge map quality assessment that aligns with human perception, though it appears incremental as it builds on existing distance-based methods by adding perceptual features.
The paper tackled the problem of evaluating edge detector performance by proposing a Just-Noticeable-Difference (JND) based quality measure to improve correlation with human judgment, and experimental results showed it outperformed existing distance-based measures in subjective evaluations.
The performance of an edge detector can be improved when assisted with an effective edge map quality measure. Several evaluation methods have been proposed resulting in different performance score for the same candidate edge map. However, an effective measure is the one that can be automated and which correlates with human judgement perceived quality of the edge map. Distance-based edge map measures are widely used for assessment of edge map quality. These methods consider distance and statistical properties of edge pixels to estimate a performance score. The existing methods can be automated; however, they lack perceptual features. This paper presents edge map quality measure based on Just-Noticeable-Difference (JND) feature of human visual system, to compensate the shortcomings of distance-based edge measures. For this purpose, we have designed constant stimulus experiment to measure the JND value for two spatial alternative. Experimental results show that JND based distance calculation outperforms existing distance-based measures according to subjective evaluation.