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.
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〉
https://hal.inria.fr/hal-01405649
Contributeur : Hal Ifip
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Soumis le : mercredi 30 novembre 2016 - 11:24:25
Dernière modification le : jeudi 16 août 2018 - 17:00:02
Document(s) archivé(s) le : lundi 27 mars 2017 - 08:05:57
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〉
