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Data compression with substitution
1989All the data compression methods described in this paper are based on substitutions acting on characters or factors occurring inside the source texts. The average expected compression ratio is often close to 2. Most methods have a bad behaviour when error appears in encoded texts. One bit lost and the decompression is almost impossible!
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2007
In this paper I describe the general principles of learning as data compression. I introduce two-part code optimization and analyze the theoretical background in terms of Kolmogorov complexity. The good news is that the optimal compression theoretically represents the optimal interpretation of the data, the bad news is that such an optimal compression ...
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In this paper I describe the general principles of learning as data compression. I introduce two-part code optimization and analyze the theoretical background in terms of Kolmogorov complexity. The good news is that the optimal compression theoretically represents the optimal interpretation of the data, the bad news is that such an optimal compression ...
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Pointwise redundancy in lossy data compression and universal lossy data compression
IEEE Transactions on Information Theory, 2000Summary: The author characterizes achievable pointwise redundancy rates for lossy data compression at a fixed distortion level. Pointwise redundancy refers to the difference between the description length achieved by an \(n\)th-order block code and the optimal \(nR(D)\) bits.
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A universal algorithm for sequential data compression
IEEE Transactions on Information Theory, 1977J. Ziv, A. Lempel
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Data Compression in the Marketplace
1997Table 1.2 in Chapter 1 presents an extensive list of applications that use data compression to store or transmit data more efficiently or to transmit it in less time. This chapter examines what the end users of those applications require, expect, and will get from data compression.
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1997
In Part III — Applications we explored many digital systems and observed that the compression process and compressed data interact with the physical system and with other logical functions in the system. This chapter takes a closer look at some issues that digital systems designers face when managing compressed data.
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In Part III — Applications we explored many digital systems and observed that the compression process and compressed data interact with the physical system and with other logical functions in the system. This chapter takes a closer look at some issues that digital systems designers face when managing compressed data.
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Music Analysis and Data Compression
2019David Meredith focuses on how the musical knowledge that underpins both music perception and musical imagery can be acquired by compressing musical information. He works from the notion that the goal of music analysis is to find the best possible explanations for the structures of musical “objects,” such as individual works or movements as well as ...
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