Compressed matching for feature vectors

Klein, Shmuel T.; Shapira, Dana

doi:10.1016/j.tcs.2015.12.021

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…This property of the appearance of a 1-bit implying that the following bit, if it exists, must be a zero, has been exploited in several useful applications: robustness to errors [13], the design of Fibonacci codes [14], direct access [15], fast decoding and compressed search [10,16], compressed matching in dictionaries [17], faster modular exponentiation [18], etc. The present work is yet another application of this idea.…”

Section: Description Of the Coding Proceduresmentioning

confidence: 99%

Context Sensitive Rewriting Codes for Flash Memory†

Klein

Shapira

2018

The Computer Journal

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Writing data on flash memory is asymmetric in the sense that it is possible to change a 0 into a 1-bit, but erasing a 1 back to value 0 is much more expensive and can only be done in blocks. This has triggered the development of rewriting codes in which new data can overwrite the old one, subject to the constraint of never changing a 1 into a zero. The notion of context-sensitive rewriting codes is introduced and we bring here the analysis of the compression performance of a family of such codes, based on generalizations of the Fibonacci sequence. This is then compared with experimental results.

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Section: Description Of the Coding Proceduresmentioning

confidence: 99%

Context Sensitive Rewriting Codes for Flash Memory†

Klein

Shapira

2018

The Computer Journal

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“…Research on lossless data compression has evolved over the years from various encoding variants, for instance [1][2][3][4][5][6][7], passing by more advanced challenges such as compressed pattern matching in texts [8,9], in images [10,11] and in structured files [12,13], and up to compact data structures [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

On the Randomness of Compressed Data

Klein

Shapira

2020

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It seems reasonable to expect from a good compression method that its output should not be further compressible, because it should behave essentially like random data. We investigate this premise for a variety of known lossless compression techniques, and find that, surprisingly, there is much variability in the randomness, depending on the chosen method. Arithmetic coding seems to produce perfectly random output, whereas that of Huffman or Ziv-Lempel coding still contains many dependencies. In particular, the output of Huffman coding has already been proven to be random under certain conditions, and we present evidence here that arithmetic coding may produce an output that is identical to that of Huffman.

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