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)


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.


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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