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Bibliografie tematiche / Data compression

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Letteratura scientifica selezionata sul tema "Data compression"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Data compression"

1

Shevchuk, Yury Vladimirovich. "Memory-efficient sensor data compression." Program Systems: Theory and Applications 13, no. 2 (2022): 35–63. http://dx.doi.org/10.25209/2079-3316-2022-13-2-35-63.

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We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tes

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Sharath, R., Sinha Shivani, B.I Supreeth, Hebbar B.C. Varun, and B. Santhosh. "Distributed Data Compression using Cloud Computing Approach." Journal of Optical Communication Electronics 5, no. 2 (2019): 5–10. https://doi.org/10.5281/zenodo.2656008.

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<em>Storage and data trafficking have grown a great deal over the past decade. Therefore, there is a need to reduce the size of the data for better speed and proper utilization of the bandwidth. There are compressions that happen online as well as some happen statically. The online based compression is in greater demand, since an online platform is much easier and faster. Static methods have a requisition of the entire file to be present at the time of transmission, while online compressions don’t. Compressions make use of an algorithm to effectively shrink the data. Depending upon the s

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Saidhbi, Sheik. "An Intelligent Multimedia Data Encryption and Compression and Secure Data Transmission of Public Cloud." Asian Journal of Engineering and Applied Technology 8, no. 2 (2019): 37–40. http://dx.doi.org/10.51983/ajeat-2019.8.2.1141.

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Data compression is a method of reducing the size of the data file so that the file should take less disk space for storage. Compression of a file depends upon encoding of file. In lossless data compression algorithm there is no data loss while compressing a file, therefore confidential data can be reproduce if it is compressed using lossless data compression. Compression reduces the redundancy and if a compressed file is encrypted it is having a better security and faster transfer rate across the network than encrypting and transferring uncompressed file. Most of the computer applications rel

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Nithya, P., T. Vengattaraman, and M. Sathya. "Survey On Parameters of Data Compression." REST Journal on Data Analytics and Artificial Intelligence 2, no. 1 (2023): 1–7. http://dx.doi.org/10.46632/jdaai/2/1/1.

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this inte

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Chen, Xinyu, Jiannan Tian, Ian Beaver, et al. "FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data." Proceedings of the VLDB Endowment 17, no. 6 (2024): 1418–31. http://dx.doi.org/10.14778/3648160.3648180.

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While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This

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Bernstein, Herbert J., Alexei Soares, Kimberly Horvat, and Jean Jakoncic. "Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis." Structural Dynamics 12, no. 2_Supplement (2025): A147. https://doi.org/10.1063/4.0000456.

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New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. Data from these detectors are always compressed losslessly, but systems may not keep up with these data rates, and the files may still be larger than seems necessary. We propose that such MX experiments will require lossy compression algorithms to keep up with data throughput and capacity for long-term storage, but note that some information may be lost. Indeed, one might employ dramatic lossy compression only for archiving of data after structure

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Ryabko, Boris. "Time-Universal Data Compression." Algorithms 12, no. 6 (2019): 116. http://dx.doi.org/10.3390/a12060116.

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Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essen

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Mishra, Amit Kumar. "Versatile Video Coding (VVC) Standard: Overview and Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 975–81. http://dx.doi.org/10.17762/turcomat.v10i2.13578.

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Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant

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McGeoch, Catherine C. "Data Compression." American Mathematical Monthly 100, no. 5 (1993): 493. http://dx.doi.org/10.2307/2324310.

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Helman, D. R., and G. G. Langdon. "Data compression." IEEE Potentials 7, no. 1 (1988): 25–28. http://dx.doi.org/10.1109/45.1889.

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Tesi sul tema "Data compression"

1

Williams, Ross Neil. "Adaptive data compression." Adelaide, 1989. http://web4.library.adelaide.edu.au/theses/09PH/09phw7262.pdf.

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2

Steinruecken, Christian. "Lossless data compression." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709134.

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Lindsay, Robert A., and B. V. Cox. "UNIVERSAL DATA COMPRESSION." International Foundation for Telemetering, 1985. http://hdl.handle.net/10150/615552.

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International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada<br>Universal and adaptive data compression techniques have the capability to globally compress all types of data without loss of information but have the disadvantage of complexity and computation speed. Advances in hardware speed and the reduction of computational costs have made universal data compression feasible. Implementations of the Adaptive Huffman and Lempel-Ziv compression algorithms are evaluated for performance. Compression ratios versus run times for different size data files

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Radhakrishnan, Radhika. "Genome data modeling and data compression." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447611.

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García, Sobrino Francisco Joaquín. "Sounder spectral data compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/663984.

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IASI (Infrared Atmospheric Sounding Interferometer) es un espectrómetro basado en la transformada de Fourier diseñado para medir radiación infrarroja emitida por La Tierra. A partir de estas mediciones se generan datos con una precisión y resolución espectral sin precedentes. Esta información es útil para obtener perfiles de temperatura y humedad, así como concentraciones de gases traza, que son esenciales para la comprensión y monitorización del clima y para realizar previsiones atmosféricas. La alta resolución espectral, espacial y temporal de los datos producidos por el instrumento impli

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Du, Toit Benjamin David. "Data Compression and Quantization." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79233.

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Data Compression Due to limitations in data storage and bandwidth, data of all types has often required compression. This need has spawned many different methods of compressing data. In certain situations the fidelity of the data can be compromised and unnecessary information can be discarded, while in other situations, the fidelity of the data is necessary for the data to be useful thereby requiring methods of reducing the data storage requirements without discarding any information. The theory of data compression has received much attention over the past half century, with some of the most i

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Roguski, Łukasz 1987. "High-throughput sequencing data compression." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/565775.

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Thanks to advances in sequencing technologies, biomedical research has experienced a revolution over recent years, resulting in an explosion in the amount of genomic data being generated worldwide. The typical space requirement for storing sequencing data produced by a medium-scale experiment lies in the range of tens to hundreds of gigabytes, with multiple files in different formats being produced by each experiment. The current de facto standard file formats used to represent genomic data are text-based. For practical reasons, these are stored in compressed form. In most cases, such storage

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Frimpong-Ansah, K. "Adaptive data compression with memory." Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/38008.

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Kretzmann, Jane Lee. "Compression of bitmapped graphic data." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25761.

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This paper explores the general topic of data compression, with emphasis on application of the techniques to graphic bitmapped data. Run-length encoding, statistical encoding (including Huffman codes), and relative encoding are examined and evaluated. A compression application of the Huffman coding of a run-length encoded file is designed and partially implemented in Chapter VII. A listing of the computer program which performs the compression is included as an appendix. Possibilities for further study are suggested. Data compression, Compression, Graphics, Bitmapped, Computer network, Run-len

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Barr, Kenneth C. (Kenneth Charles) 1978. "Energy aware lossless data compression." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87316.

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Più fonti

Libri sul tema "Data compression"

1

Salomon, David. Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9.

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Salomon, David. Data Compression. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8.

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Huang, Bormin, ed. Satellite Data Compression. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1183-3.

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Williams, Ross N. Adaptive Data Compression. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5.

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5

Motta, Giovanni, Francesco Rizzo, and James A. Storer, eds. Hyperspectral Data Compression. Kluwer Academic Publishers, 2006. http://dx.doi.org/10.1007/0-387-28600-4.

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Huang, Bormin. Satellite data compression. Springer Science+Business Media, LLC, 2011.

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Giovanni, Motta, Rizzo Francesco, and Storer James A. 1953-, eds. Hyperspectral data compression. Springer, 2005.

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Williams, Ross N. Adaptive Data Compression. Kluwer Academic Publishers, 1991.

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9

Mark, Nelson. The data compression book: Featuring fast, efficient data compresssion techniques in C. M & T, 1991.

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Salomon, David, and Giovanni Motta. Handbook of Data Compression. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-903-9.

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Capitoli di libri sul tema "Data compression"

1

Salomon, David. "Image Compression." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_4.

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Salomon, David. "Image Compression." In Data Compression. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_5.

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Salomon, David. "Video Compression." In Data Compression. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_7.

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Salomon, David. "Audio Compression." In Data Compression. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_8.

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Salomon, David. "Basic Techniques." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_1.

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Salomon, David. "Error Correcting Codes." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_10.

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Salomon, David. "Fourier Transform." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_11.

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Salomon, David. "Group 4 Codes Summary." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_12.

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Salomon, David. "Hashing." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_13.

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Salomon, David. "Interpolating Polynomials." In Data Compression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_14.

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Atti di convegni sul tema "Data compression"

1

Qin, Liang, and Jie Sun. "Model Compression for Data Compression: Neural Network Based Lossless Compressor Made Practical." In 2023 Data Compression Conference (DCC). IEEE, 2023. http://dx.doi.org/10.1109/dcc55655.2023.00013.

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Marin, Jeison, Leonardo Betancur, and Henry Arguello. "Compression Ratio Design in Compressive Spectral Imaging." In 2016 Data Compression Conference (DCC). IEEE, 2016. http://dx.doi.org/10.1109/dcc.2016.81.

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"Author Index." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.19.

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Sangho Yoon, Chee Sun Won, Kyungsuk Pyun, and R. M. Gray. "Image classification using GMM with context information and with a solution of singular covariance problem." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1194076.

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"Author index." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1194078.

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"Proceedings. DCC 2005. Data Compression Conference." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.28.

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"Table of Contents." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.84.

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"Title Page." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.89.

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"Proceedings DCC 2003. Data Compression Conference." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1193990.

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Mishali, Moshe, and Yonina C. Eldar. "Xampling: Analog Data Compression." In 2010 Data Compression Conference. IEEE, 2010. http://dx.doi.org/10.1109/dcc.2010.39.

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Rapporti di organizzazioni sul tema "Data compression"

1

Creusere, Charles D., and Jim Witham. Data Compression Project. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada370497.

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Pan, David. Efficient Data Compression Techniques for Weather Data. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada540395.

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Choi, Junho, and Mitchell R. Grunes. Lossless Data Compression of Packet Data Streams,. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada304792.

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Gryder, R., and K. Hake. Survey of data compression techniques. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/10107839.

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Gryder, R., and K. Hake. Survey of data compression techniques. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5926128.

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Duff, C. R. W. Data compression and computation speed. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/315270.

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Clark, D. Argon Excluder Foam Compression Data. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/1031764.

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Horan, Shield. Data Compression Techniques to Reduce Bandwidth. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada399290.

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Senecal, Joshua G. Length-Limited Data Transformation and Compression. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/877882.

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Perkins, William W. Data Compression With Application to Geo-Location. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532376.

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