Abstract
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.
Highlights
Binary image encoding plays a key role in applications such as image analysis, matching, and retrieval
Spline-based Medial Axis Transform (SMAT) depends on two parameters (Fig. 2): the salience threshold σ0, which gives the simplification of the medial axis SI, and the tolerance γ0 that tells how accurately B-splines fit medial branches
We evaluate SMAT based on two factors: Similarity Q of the reconstruction Iprovided by SMAT
Summary
Binary image encoding plays a key role in applications such as image analysis, matching, and retrieval. It is important for the compression of images and videos [1]. Bitmap techniques encode the pixels of an image as belonging to the shape (foreground) or outside it (background). These include the modified READ method [11], based on run-length encoding, and context-based arithmetic encoding [12], an efficient entropy coding scheme, which was adopted by the MPEG-4 standards [13]
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