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: Saeed, K., Snášel, V. (eds) Computer Information Systems and Industrial Management. CISIM 2015. Lecture Notes in Computer Science, vol 8838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45237-0_52
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DOI: https://doi.org/10.1007/978-3-662-45237-0_52
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