Subspaces Clustering Approach to Lossy Image Compression

  • Przemysław Spurek
  • Marek Śmieja
  • Krzysztof Misztal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8838)

Abstract

In this contribution lossy image compression based on subspaces clustering is considered. Given a PCA factorization of each cluster into subspaces and a maximal compression error, we show that the selection of those subspaces that provide the optimal lossy image compression is equivalent to the 0-1 Knapsack Problem. We present a theoretical and an experimental comparison between accurate and approximate algorithms for solving the 0-1 Knapsack problem in the case of lossy image compression.

Keywords

lossy compression image compression subspaces clustering 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Przemysław Spurek
    • 1
  • Marek Śmieja
    • 1
  • Krzysztof Misztal
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceJagiellonian UniversityKrakówPoland
  2. 2.Faculty of Physics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland

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