Lossless data compression algorithms based on substitution tables

2002 ◽  
Author(s):  
J.C. Kieffer ◽  
En-hui Yang
Keyword(s):  
Data Compression ◽  
Compression Algorithms ◽  
Lossless Data Compression

scholarly journals Comparison Study of Lossless Data Compression Algorithms for Text Data

2013 ◽  
Vol 11 (6) ◽  
pp. 15-19 ◽  
Author(s):  
Arup Kumar Bhattacharjee Arup Kumar Bhattacharjee
Keyword(s):  
Data Compression ◽  
Comparison Study ◽  
Text Data ◽  
Compression Algorithms ◽  
Lossless Data Compression

scholarly journals FPGA implementation of Lempel-Ziv data compression

2021 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Gody Mostafa ◽  
Abdelhalim Zekry ◽  
Hatem Zakaria
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Digital Communication ◽  
Compression Rate ◽  
High Compression Ratio ◽  
Compression Algorithms ◽  
High Compression ◽  
Space Saving ◽  
Fpga Chip ◽  
Lossless Data Compression

When transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data. In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding. One of the most lossless data compression algorithms commonly used is Lempel-Ziv. The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach. The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip. The results show that our design is efficient in providing high compression rates and space-saving percentage as well as improved utilization. The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs.

scholarly journals Constructing Binary Huffman Tree

2013 ◽  
Vol 21 (2) ◽  
pp. 133-143
Author(s):  
Hiroyuki Okazaki ◽  
Yuichi Futa ◽  
Yasunari Shidama
Keyword(s):  
Data Compression ◽  
Binary Code ◽  
Lossless Compression ◽  
Huffman Coding ◽  
Compression Algorithms ◽  
Huffman Encoding ◽  
Lossless Data Compression ◽  
Entropy Encoding

Summary Huffman coding is one of a most famous entropy encoding methods for lossless data compression [16]. JPEG and ZIP formats employ variants of Huffman encoding as lossless compression algorithms. Huffman coding is a bijective map from source letters into leaves of the Huffman tree constructed by the algorithm. In this article we formalize an algorithm constructing a binary code tree, Huffman tree.

scholarly journals ΔRLE

2021 ◽  
Vol 45 (1) ◽  
pp. 329-349
Author(s):  
Branslav Mados ◽  
Zuzana Bilanová ◽  
Ján Hurtuk
Keyword(s):  
Data Compression ◽  
Simultaneous Increase ◽  
Original Form ◽  
Memory Consumption ◽  
Compression Efficiency ◽  
Compression Algorithms ◽  
Run Length ◽  
Lossless Data Compression ◽  
Temporal And Spatial ◽  
Basic Features

Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.

A Lossless Data Compression Algorithm for Wireless Sensor Networks Based on Linear Regression Model

2011 ◽  
Vol 403-408 ◽  
pp. 2441-2444
Author(s):  
Hong Zhi Lu ◽  
Xue Jun Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Data Compression ◽  
Linear Regression Model ◽  
Compression Algorithm ◽  
Sensor Data ◽  
Simple Linear Regression ◽  
Compression Algorithms ◽  
One Dimensional ◽  
Lossless Data Compression

According to the theory of simple linear regression model, this paper designed a lossless sensor data compression algorithm based on one-dimensional linear regression model. The algorithm computes the linear fitting values of sensor data’s differences and fitting residuals, which are input to a normal distribution entropy encoder to perform compression. Compared with two typical lossless compression algorithms, the proposed algorithm indicated better compression ratios.

Stationary Stochastic Processes and Fractal Data Compression

1997 ◽  
Vol 07 (03) ◽  
pp. 551-567 ◽  
Author(s):  
Michael F. Barnsley ◽  
Anca Deliu ◽  
Ruifeng Xie
Keyword(s):  
Data Compression ◽  
Iterated Function System ◽  
Geometrical Approach ◽  
Compression Method ◽  
Compression Algorithms ◽  
Piecewise Affine ◽  
Affine Mappings ◽  
Iterated Function ◽  
Stationary Stochastic Processes ◽  
Lossless Data Compression

It is shown that the invariant measure of a stationary nonatomic stochastic process yields an iterated function system with probabilities and an associated dynamical system that provide the basis for optimal lossless data compression algorithms. The theory is illustrated for the case of finite-order Markov processes: For a zero-order process, it produces the arithmetic compression method; while for higher order processes it yields dynamical systems, constructed from piecewise affine mappings from the interval [0, 1] into itself, that may be used to store information efficiently. The theory leads to a new geometrical approach to the development of compression algorithms.

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