Spline-Based Dense Medial Descriptors for Lossy Image Compression

Medial descriptors are of significant interest for image simplification, representation, manipulation, and compression. On the other hand, B-splines are well-known tools for specifying smooth curves in computer graphics and geometric design. In this paper, we integrate the two by modeling medial descriptors with stable and accurate B-splines for image compression. Representing medial descriptors with B-splines can not only greatly improve compression but is also an effective vector representation of raster images. A comprehensive evaluation shows that our Spline-based Dense Medial Descriptors (SDMD) method achieves much higher compression ratios at similar or even better quality to the well-known JPEG technique. We illustrate our approach with applications in generating super-resolution images and salient feature preserving image compression.

A SCALE-SPACE TOGGLE OPERATOR FOR IMAGE TRANSFORMATIONS

The analysis of different representation levels has been largely used in several image analysis tasks to handle the multiscale nature of image data, allowing the extraction of specific features that become explicit at each scale. In this work, we explore the scale-space properties of a self-dual toggle operator defined on a scaled morphological framework. These properties conduce to a well-controlled image simplification where its maxima and minima interact at the same time during pixels' transformation, in contrast to other approaches that consider these extrema separately. In such a way, it is possible to identify significant image extrema information to be used in several high level tasks. To assess the robustness of our approach, we carry out tests on images of several classes and subjected to different lighting conditions for various applications, including segmentation and binarization.