Compressing Images with Diffusion- and Exemplar-Based Inpainting

Conference paper

DOI: 10.1007/978-3-319-18461-6_13

Volume 9087 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Peter P., Weickert J. (2015) Compressing Images with Diffusion- and Exemplar-Based Inpainting. In: Aujol JF., Nikolova M., Papadakis N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2015. Lecture Notes in Computer Science, vol 9087. Springer, Cham

Abstract

Diffusion-based image compression methods can surpass state-of-the-art transform coders like JPEG 2000 for cartoon-like images. However, they are not well-suited for highly textured image content. Recently, advances in exemplar-based inpainting have made it possible to reconstruct images with non-local methods from sparse known data. In our work we compare the performance of such exemplar-based and diffusion-based inpainting algorithms, dependent on the type of image content. We use our insights to construct a hybrid compression codec that combines the strengths of both approaches. Experiments demonstrate that our novel method offers significant advantages over state-of-the-art diffusion-based methods on textured image data and can compete with transform coders.

Keywords

Exemplar-based inpainting Diffusion-based inpainting Image compression Texture 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany