Dense Skeletons for Image Compression and Manipulation

Terpstra, M.L. (2017) Dense Skeletons for Image Compression and Manipulation. Master's Thesis / Essay, Computing Science.

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Abstract

Skeletons are well-known, compact 2D or 3D shape descriptors. Earlier, skeletons have been extended to dense skeletons to encode grayscale images rather than binary images. To do this an image is decomposed in threshold sets which are skeletonized individually. So far, storing images using this approach has not been able to compete with common image compression algorithms such as JPEG. In this work we attempt to improve the compression quality by exploiting the structure of dense skeletons in order to reduce redundancy and by using sophisticated encoding schemes. We compare these images with conventional image compression methods in terms of size and quality. Moreover, we research the effects of combining well-established image compressors our dense skeleton results. Previous works have also shown that interesting stylistic effects can occur when an image is processed using dense skeletons. We attempt to introduce new image manipulation techniques by performing skeleton bundling. With these operations it can become possible to alter image lighting and perform further image simplification. We will research how these manipulation techniques can influence image size, image quality and how these can create new, interesting effects. We show that we can reliably generate images using our pipeline of high fidelity at a file size smaller than JPEG using our dense skeleton image encoding and can generate images of very high fidelity at a file size smaller than JPEG by using our method as a JPEG preprocessor. We demonstrate the effects of inter-layer skeleton path bundling as a local contrast enhancement method which generates interesting effect. We also demonstrate that our pipeline can generate extremely simplified representations of images, and extend our method to color images.

Item Type:	Thesis (Master's Thesis / Essay)
Degree programme:	Computing Science
Thesis type:	Master's Thesis / Essay
Language:	English
Date Deposited:	15 Feb 2018 08:26
Last Modified:	15 Feb 2018 08:26
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/14869

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