PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric
This provides a domain-specific solution for applications requiring structural analysis of binary images at different resolutions, but it is incremental as it builds on existing metrics like SSIM.
The paper tackled the problem of comparing binary images across varying resolutions by introducing PointSSIM, a low-dimensional, resolution-invariant metric based on structural similarity and mathematical morphology, which efficiently captures structural attributes through anchor points and summary vectors.
This paper presents PointSSIM, a novel low-dimensional image-to-image comparison metric that is resolution invariant. Drawing inspiration from the structural similarity index measure and mathematical morphology, PointSSIM enables robust comparison across binary images of varying resolutions by transforming them into marked point pattern representations. The key features of the image, referred to as anchor points, are extracted from binary images by identifying locally adaptive maxima from the minimal distance transform. Image comparisons are then performed using a summary vector, capturing intensity, connectivity, complexity, and structural attributes. Results show that this approach provides an efficient and reliable method for image comparison, particularly suited to applications requiring structural analysis across different resolutions.