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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Communication dans un congrès
Khalid Saeed; Václav Snášel. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. Springer, Lecture Notes in Computer Science, LNCS-8838, pp.571-579, 2014, Computer Information Systems and Industrial Management. 〈10.1007/978-3-662-45237-0_52〉
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Przemysław Spurek, Marek Śmieja, Krzysztof Misztal. Subspaces Clustering Approach to Lossy Image Compression. Khalid Saeed; Václav Snášel. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. Springer, Lecture Notes in Computer Science, LNCS-8838, pp.571-579, 2014, Computer Information Systems and Industrial Management. 〈10.1007/978-3-662-45237-0_52〉. 〈hal-01405649〉

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