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Image Compression Using Data-Dependent Triangulations

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4841))

Abstract

We present a method to speed up the computation of a high-quality data-dependent triangulation approximating an image using simulated annealing by probability distributions guided by local approximation error and its variance. The triangulation encodes the image, yielding compression rates comparable to or even superior to JPEG and JPEG2000 compression.

The specific contributions of our paper are a speed-up of the simulated annealing optimization and a comparison of our approach to other image approximation and compression methods. Furthermore, we propose an adaptive vertex insertion/removal strategy and termination criteria for the simulated annealing to achieve specified approximation error bounds.

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George Bebis Richard Boyle Bahram Parvin Darko Koracin Nikos Paragios Syeda-Mahmood Tanveer Tao Ju Zicheng Liu Sabine Coquillart Carolina Cruz-Neira Torsten Müller Tom Malzbender

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© 2007 Springer-Verlag Berlin Heidelberg

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Lehner, B., Umlauf, G., Hamann, B. (2007). Image Compression Using Data-Dependent Triangulations. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76858-6_35

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  • DOI: https://doi.org/10.1007/978-3-540-76858-6_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76857-9

  • Online ISBN: 978-3-540-76858-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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