Subspaces Clustering Approach to Lossy Image Compression

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.
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Przemysław Spurek, Marek Śmieja, Krzysztof Misztal. Subspaces Clustering Approach to Lossy Image Compression. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. pp.571-579, ⟨10.1007/978-3-662-45237-0_52⟩. ⟨hal-01405649⟩

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