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New lossless compression method for BMP true color images
Published in Xiaoling Jia, Feng Wu, Electromechanical Control Technology and Transportation, 2017
ZIP is a compression file format that supports the lossless data compression. ZIP file format permits many compression algorithms, of which DEFLATE are the most common one. This format was originally created in 1989 by Phil Katz (Guo 2015), and was first used in PKWARE, Inc.’s PKZIP utility, as a replacement for the previous ARC compression format by Thom Henderson. ZIP is widely used in the fields of compression program and data application. Each byte must be recovered accurately to ensure the integrity and reliability of the information while decompressing.
Metaheuristic-Based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks
Published in Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset, Artificial Intelligence Techniques in IoT Sensor Networks, 2020
S. Dhanasekaran, I. S. Hephzi Punithavathi, P. Duraipandy, A. Sivanesh Kumar, P. Vijayakarthik, S. Rajasekaran, B. S. Murugan
Deflate is referred as lossless compression technique which has been extremely utilized over an extended time duration because of its maximum speed and optimal compression effectiveness [16]. Several techniques such as GZIP, ZLIB, ZIP, and PKZIP depend on the Deflate compression technique. These techniques contain LZ77 method and Huffman coding. The original information undergoes initial compression utilizing the LZ77 technique and after that the data is even reduced by the Huffman technique.
Multimedia Data Compression
Published in Sreeparna Banerjee, Elements of Multimedia, 2019
A significantly better compression rate can be achieved by combining LZ77 with an additional entropy coding algorithm, for example, the Huffman or Shannon-Fano coding. The widespread deflate compression method (e.g., for GZIP or ZIP) uses Huffman codes.
Feature Selection for Supervised Learning and Compression
Published in Applied Artificial Intelligence, 2022
Phillip Taylor, Nathan Griffiths, Vince Hall, Zhou Xu, Alex Mouzakitis
Lossless compression aims to compress the data in such a way that the uncompressed version is indistinguishable from the original (Salomon and Motta 2010). Typically, lossless compression inspects the frequencies of symbols and looks for repeating symbols or sequences of symbols in the data stream. Perhaps the most simple method of compression is run-length encoding, in which symbols are encoded along with their number of consecutive repetitions. For example, the string ‘AAAABBA’ can be encoded as ‘A4B2A1.’ Two other notable lossless compression algorithms are LZ77 dictionary encoding (Ziv and Lempel 1978) and Huffman coding (Huffman 1952). LZ77 uses a sliding window and searches for repeating sequences, which are encoded as the length and location of its first occurrence in the window. Huffman coding produces a variable length prefix-code defining the path to the encoded symbol in a Huffman tree. Symbols that occur with higher frequencies are located closer to the root node in the tree and thus have shorter Huffman codes. Taken together, LZ77 and Huffman encoding make up the DEFLATE compression algorithm (Deutsch 1996), which is the basis of the ZIP file format.
A comprehensive optimization strategy for real-time spatial feature sharing and visual analytics in cyberinfrastructure
Published in International Journal of Digital Earth, 2019
Text data compression itself is a very active research topic. Classic data compression algorithms include: Run-length encoding (RLE; Robinson and Cherry 1967), Burrows–Wheeler transform (Burrows and Wheeler 1994), Huffman coding (Huffman 1952), Prediction by partial matching (PPM; Cleary and Witten 1984), LZ77 (Ziv and Lempel 1977), LZ78 (Ziv and Lempel 1978), etc. Currently, there are dozens of available data compression methods and toolkits derived from these algorithms. In consideration of the requirements for data interoperability and performance optimization, the target data compression methods for WFS should possess the characteristics of (1) robust and well performed in terms of compression speed and compression ratio; (2) widely adopted; (3) have available software development kits (SDK) for both server and client sides integration. The DEFLATE (Deutsch 1996) and LZMA (Lempel–Ziv–Markov chain; Pavlov 2007) algorithms are selected for integration and testing in this research as both are widely adopted. The DEFLATE algorithm is a combination of LZ77 and Huffman encoding. While the LZMA algorithm is a derivation of LZ77. Generally, the DEFLATE method compresses files faster than LZMA, but the generated files have less compression ratio (Li et al. 2015).
SARTRES: a semi-autonomous robot teleoperation environment for surgery
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
Md Masudur Rahman, Mythra V. Balakuntala, Glebys Gonzalez, Mridul Agarwal, Upinder Kaur, Vishnunandan L. N. Venkatesh, Natalia Sanchez-Tamayo, Yexiang Xue, Richard M. Voyles, Vaneet Aggarwal, Juan Wachs
Since the sequence is not generated by a memory less source, direct calculation of is non-trivial. From Shannon Source Coding Theorem, we note that, for large sequences, an efficient compression algorithm compresses the sequence to the optimal number of bits required which is equal to the entropy of the sequence (Cover and Thomas 2012). Hence, we measure the entropy using the data required to efficiently store . We use DEFLATE algorithm (Larsson 1996) to compress our data, further, we use gzip implementation for using DEFLATE algorithm.