The methods of improving the compression ratio of LZ77 family data compression algorithms

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.

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Second compression for pixelated images under edge-based compression algorithms: JPEG-LS as an example

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201563 ◽

2021 ◽

pp. 1-9

Author(s):

Kamal Al-Khayyat ◽

Imad Al-Shaikhli ◽

Mohamad Al-Hagery

Keyword(s):

Data Compression ◽

Compression Ratio ◽

Lossless Compression ◽

General Purpose ◽

Compression Algorithm ◽

Random Data ◽

Data Set ◽

Compression Algorithms ◽

Edge Based

This paper details the examination of a particular case of data compression, where the compression algorithm removes the redundancy from data, which occurs when edge-based compression algorithms compress (previously compressed) pixelated images. The newly created redundancy can be removed using another round of compression. This work utilized the JPEG-LS as an example of an edge-based compression algorithm for compressing pixelated images. The output of this process was subjected to another round of compression using a more robust but slower compressor (PAQ8f). The compression ratio of the second compression was, on average, 18%, which is high for random data. The results of the second compression were superior to the lossy JPEG. Under the used data set, lossy JPEG needs to sacrifice 10% on average to realize nearly total lossless compression ratios of the two-successive compressions. To generalize the results, fast general-purpose compression algorithms (7z, bz2, and Gzip) were used too.

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Intensification of Lempel-ZIV-Welch Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1092.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 587-591

Keyword(s):

Data Compression ◽

Compression Ratio ◽

Text Compression ◽

Code Length ◽

File Size ◽

Compression Scheme ◽

Compression Algorithms ◽

Original Algorithm ◽

Using Data

There is a necessity to reduce the consumption of exclusive resources. This is achieved using data compression. The data compression is one well known technique which can reduce the file size. A plethora of data compression algorithms are available which provides compression in various ratios. LZW is one of the powerful widely used algorithms. This paper attempts to propose and apply some enhancements to LZW, hence comes out with an efficient lossless text compression scheme that can compress a given file at better compression ratio. The paper proposes three approaches which practically enhances the original algorithm. These approaches try to gain better compression ratio. In approach1, it exploits the notion of using existing string code with odd code for a newly encounter string which is reverse of existing. In approach2 it uses a choice of code length for the current compression, so avoiding the problem of dictionary overflow. In approach3 it appends some selective set of frequently encountered string patterns. So the intensified LZW method provides better compression ratio with the inclusion of the above features.

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Performance Evaluation of Data Compression Algorithms for IoT-Based Smart Water Network Management Applications

Journal of Applied Science & Process Engineering ◽

10.33736/jaspe.2272.2020 ◽

2020 ◽

Vol 7 (2) ◽

pp. 554-563

Author(s):

Kazeem B. Adedeji

Keyword(s):

Performance Evaluation ◽

Water Supply ◽

Network Management ◽

Data Compression ◽

Compression Ratio ◽

Water Network ◽

Compression Algorithms ◽

Compression Factor ◽

Smart Water ◽

Lzw Algorithm

IoT-based smart water supply network management applications generate a huge volume of data from the installed sensing devices which are required to be processed (sometimes in-network), stored and transmitted to a remote centre for decision making. When the volume of data produced by diverse IoT smart sensing devices intensify, processing and storage of these data begin to be a serious issue. The large data size acquired from these applications increases the computational complexities, occupies the scarce bandwidth of data transmission and increases the storage space. Thus, data size reduction through the use of data compression algorithms is essential in IoT-based smart water network management applications. In this paper, the performance evaluation of four different data compression algorithms used for this purpose is presented. These algorithms, which include RLE, Huffman, LZW and Shanon-Fano encoding were realised using MATLAB software and tested on six water supply system data. The performance of each of these algorithms was evaluated based on their compression ratio, compression factor, percentage space savings, as well as the compression gain. The results obtained showed that the LZW algorithm shows better performance base on the compression ratio, compression factor, space savings and the compression gain. However, its execution time is relatively slow compared to the RLE and the two other algorithms investigated. Most importantly, the LZW algorithm has a significant reduction in the data sizes of the tested files than all other algorithms

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