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Subspaces clustering approach to lossy image compression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014) 8838 571-579
DOI: 10.1007/978-3-662-45237-0_52
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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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Spurek, P., Śmieja, M., & Misztal, K. (2014). Subspaces clustering approach to lossy image compression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8838, pp. 571–579). Springer Verlag. https://doi.org/10.1007/978-3-662-45237-0_52

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