A universal algorithm for sequential data compression

By J. Ziv, A. Lempel. 1977

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Citations in the corpus

Causal Markov condition for submodular information measures (2010, background): “The underlying measure of information is motivated by universal compression algorithms like LZ77 [15] and grammar based compression [16] that detect repeated occurrences of identical substrings within a given input string and encode them more efﬁciently.”
Transfer without Forgetting (2022, positive): “We finally point out that, as maps m take discrete binary values, one could profit from lossless compression algorithms (such as Run-Length Encoding [54] or LZ77 [71]) and thus store a compressed representation into the memory buffer.”
Compress and Control (2014, methodology): “Well known non-probabilistic compression methods such as LEMPEL-ZIV (Ziv and Lempel 1977) can be used by forming their associated coding distribution 2−`z(x1:n), where `z(x1:n) is the length of the compressed data x1:n in bits under compression method z.”
Sequential neural text compression (1996, methodology): “[2]), while \compress" and \gzip" are based on the asymptotically \optimal" Lempel-Ziv technique [13].”
Replacing suffix trees with enhanced suffix arrays (2004, methodology): “As a second application of the bottom-up traversal of the lcp-interval tree, we will very briefly describe how to compute the Ziv–Lempel decomposition [33,34] of a string.”
A corpus for the evaluation of lossless compression algorithms (1997, background): “pression [CH87]), gzip (a public domain utility derived from LZ77 [ ZL77 ]), bred (a sort based compression system [BW94]), and PPMC (Prediction by Partial Matching [Mof90]).”
Detecting visually similar Web pages: Application to phishing detection (2010, methodology): “4.2 Compression Algorithms and Supersignals The most commonly used data compression programs are gzip, bzip2, and PPM. Gzip is a Lempel-Ziv-type compressor with a 32-kilobyte window [Ziv and Lempel 1977].”
Energy-Aware Design of Compressed Sensing Systems for Wireless Sensors Under Performance and Reliability Constraints (2013, methodology): “We also show that the degradation in reconstruction performance due to channel errors is gradual for CS, whereas standard lossless source coding algorithms such as Huffman [20] and Lempel-Ziv (LZ) [21] are susceptible to error propagation when decoding corrupted compressed data [22].”
Integrating compression and execution in column-oriented database systems (2006, methodology): “Lempel-Ziv ([35, 36]) compression is the most widely used technique for lossless file compression.”
Fast lossless compression of scientific floating-point data (2006, background): “Huffman [24], LZ77 [ 35 ], and Prediction by Partial Matching [32] algorithms.”
Polar Codes for Channel and Source Coding (2009, background): “The Lempel-Ziv algorithm [34, 35] solves the problem for the wide class of stationary ergodic sources.”
Methods derived from nonlinear dynamics for analysing heart rate variability (2009, background): “Ziv & Lempel (1977) introduced a universal algorithm for lossless data compression (CE), using string matching on a sliding window.”
A compression algorithm for DNA sequences and its applications in genome comparison (2000, methodology): “4 GenCompress: an algorithm based on approximate matching Lempel and Ziv proposed two algorithms in [21, 11] to compress universal data sequences.”
Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, methodology): “, [77]), and Lempel-Ziv complexity [78, 79, 80, 81].”
Fast Error-Bounded Lossy HPC Data Compression with SZ (2016, methodology): “Typical lossless compressors include Gzip [5], LZ77 [24], Huffman encoding [33], FPC [9] and Fpzip [12].”