Abstract
Compression is most important when space is in short supply, so compression algorithms are often implemented in limited memory. Most analyses ignore memory constraints as an implementation detail, however, creating a gap between theory and practice. In this paper we consider the effect of memory limitations on compression algorithms. In the first part of the paper we assume the memory available is fixed and prove nearly tight upper and lower bound on how much is needed to compress a string close to its k-th order entropy. In the second part we assume the memory available grows (slowly) as more and more characters are read. In this setting we show that the rate of growth of the available memory determines the speed at which the compression ratio approaches the entropy. In particular, we establish a relationship between the rate of growth of the sliding window in the LZ77 algorithm and its convergence rate.
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Gagie, T., Manzini, G. (2007). Space-Conscious Compression. In: Kučera, L., Kučera, A. (eds) Mathematical Foundations of Computer Science 2007. MFCS 2007. Lecture Notes in Computer Science, vol 4708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74456-6_20
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DOI: https://doi.org/10.1007/978-3-540-74456-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74455-9
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