Results 11 to 20 of about 365,915 (264)
On the Randomness of Compressed Data [PDF]
2019 Data Compression Conference (DCC), 2019 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 ...
Shmuel T. Klein, Dana Shapira
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Toward Bayesian Data Compression [PDF]
Annalen der Physik, 2021 AbstractIn order to handle large datasets omnipresent in modern science, efficient compression algorithms are necessary. Here, a Bayesian data compression (BDC) algorithm that adapts to the specific measurement situation is derived in the context of signal reconstruction.
Johannes Harth‐Kitzerow +3 more
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Digital watermarking method based on heteroassociative image compression and its realization with artificial neural networks [PDF]
Компьютерная оптика, 2018 In this paper, we present a digital watermarking method and associated algorithms that use a heteroassociative compressive transformation to embed a digital watermark bit sequence into blocks (fragments) of container images.
Alexander Sirota +2 more
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Trajectory Data Compression Algorithm Based on Ship Navigation State and Acceleration Variation
Journal of Marine Science and Engineering, 2023 An active area of study under the dual carbon target, which is based on automatic identification systems (AIS), is the emission inventory of pollutants from ships.
Junbo Gao, Ze Cai, Wangjing Yu, Wei Sun
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ACM Computing Surveys, 1987 This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano, and Huffman in the late 1940s to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density.
Lelewer, Debra A., Hirschberg, Daniel S.
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Improved Edge Folding Algorithm for 3D Building Models Taking into Account the Visual Features
Buildings, 2023 Simplifying 3D building models, effectively reducing model complexity and improving mapping efficiency, is an important part of 3D GIS. In order to address the problem that the simplification basis considered by most 3D building model data compression ...
Haoyuan Bai +4 more
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Interactive Compression of Digital Data
Algorithms, 2010 If we can use previous knowledge of the source (or the knowledge of a source that is correlated to the one we want to compress) to exploit the compression process then we can have significant gains in compression.
Bruno Carpentieri
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A tight upper bound on the size of the antidictionary of a binary string [PDF]
Discrete Mathematics & Theoretical Computer Science, 2005 A tight upper bound of the size of the antidictionary of a binary string is presented. And it is shown that the size of the antidictionary of a binary sting is always smaller than or equal to that of its dictionary.
Hiroyoshi Morita, Takahiro Ota
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Average Redundancy for Known Sources: Ubiquitous Trees in Source Coding [PDF]
Discrete Mathematics & Theoretical Computer Science, 2008 Analytic information theory aims at studying problems of information theory using analytic techniques of computer science and combinatorics. Following Hadamard's precept, these problems are tackled by complex analysis methods such as generating functions,
Wojciech Szpankowski
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An Introduction to Neural Data Compression
Foundations and Trends® in Computer Graphics and Vision, 2023 Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational
Yibo Yang, Stephan Mandt, Lucas Theis
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