Dense Medial Descriptors for Image Compression and Manipulation

With the unprecedented growth in resolution, diversity, and applications of image data, the demand for efficient and effective image compression and manipulation is ever increasing. In this thesis, we first explore the possibilities of dense skeletons, represented suitably by raster or vector models, using saliency maps or not, for lossy image compression. Qualitative and quantitative evaluations show that our skeleton-based image compression methods achieve better trade-offs of image quality vs compression than JPEG on a wide variety of image types, and produce comparable results to JPEG 2000 and BPG on certain image types. In the second part, we show how spline-based dense skeletons, when combined with morphological trees, can be effective and efficient tools for manipulating grayscale or color images by showing various applications, including super-resolution image generation, watermark removal, and image deformation. We finally conclude that dense skeletons are a valuable addition to the toolkit of image processing researchers and practitioners

Spline-based medial axis transform representation of binary images

Medial axes are well-known descriptors used for representing, manipulating, and compressing binary images. In this paper, we present a full pipeline for computing a stable and accurate piece-wise B-spline representation of Medial Axis Transforms (MATs) of binary images. A comprehensive evaluation on a benchmark shows that our method, called Spline-based Medial Axis Transform (SMAT), achieves very high compression ratios while keeping quality high. Compared with the regular MAT representation, the SMAT yields a much higher compression ratio at the cost of a slightly lower image quality. We illustrate our approach on a multi-scale SMAT representation, generating super-resolution images, and free-form binary image deformation