Analysis for Lossless Data Compression Algorithms for Low Bandwidth Networks

Semunigus, Wogderes; Pattanaik, Balachandra

doi:10.1088/1742-6596/1964/4/042046

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…With the lossless compression method, the original data from the file is maintained without any damage or data loss when the data is not compressed. With this advantage, the lossless compression method is very suitable in compressing with text file types, where the data or information contained in it is very important [11].…”

Section: Lossless Compressionmentioning

confidence: 99%

Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression

Fauzan

Alif²,

Prianto3³

2022

ITJRD

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The development of data storage hardware is very rapidly over time. In line with the development of storage hardware, the amount of digital data shared on the internet is increasing every day. That way no matter how big the size of the storage device we have, of course, it will only be a matter of time until that storage space is exhausted. Therefore, in terms of maximizing storage space, a technique called compression emerged. This study focuses on a comparative analysis of 2 Lossless compression technique algorithms, namely the Huffman algorithm and Lempel Ziv Welch (LZW). A number of test files with different file types are applied to both algorithms that are compared. The performance of the algorithm is determined based on the comparison of space saving and compression time. The test results showed that the Lempel Ziv Welch (LZW) algorithm was superior to Huffman’s algorithm in .txt file type compression and .csv, the average space savings produced were 63.85% and 77.56%. The degree of compression speed that each algorithm produces is directly proportional to the file size.

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Section: Lossless Compressionmentioning

confidence: 99%

Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression

Fauzan

Alif²,

Prianto3³

2022

ITJRD

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“…[7] proposed the performance improvement of image compression method using hybridization of lossy and lossless method. [8] based their research on the low bandwidth networks. They tested the various lossless image compression algorithms and compared Huffman, LZW and RLE algorithms which they concluded that Huffman performs better on huge data than LZW and RLE algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Image Compression and Processing Scheme

Manga

Garba

Ahmadu

2021

CJAST

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Image compression refers to the process of encoding image using fewer number of bits. The major aim of lossless image compression is to reduce the redundancy and irreverence of image data for better storage and transmission of data in the better form. The lossy compression scheme leads to high compression ratio while the image experiences lost in quality. However, there are many cases where the loss of image quality or information due to compression needs to be avoided, such as medical, artistic and scientific images. Efficient lossless compression become paramount, although the lossy compressed images are usually satisfactory in divers’ cases. This paper titled Enhanced Lossless Image Compression Scheme is aimed at providing an enhanced lossless image compression scheme based on Bose, Chaudhuri Hocquenghem- Lempel Ziv Welch (BCH-LZW) lossless image compression scheme using Gaussian filter for image enhancement and noise reduction. In this paper, an efficient and effective lossless image compression technique based on LZW- BCH lossless image compression to reduce redundancies in the image was presented and image enhancement using Gaussian filter algorithm was demonstrated. Secondary method of data collection was used to collect the data. Standard research images were used to validate the new scheme. To achieve these, an object approach using Java net beans was used to develop the compression scheme. From the findings, it was revealed that the average compression ratio of the enhanced lossless image compression scheme was 1.6489 and the average bit per pixel was 5.416667. Gaussian filter image enhancement was used for noise reduction and the image was enhanced eight times the original.

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Performance Analysis of Compression Algorithms on Matrix Data: Data Transfer Optimization in Microservices Architectures

Atasoy,

Akkaya,

Kocakır

et al. 2024

Türk Doğa Ve Fen Dergisi

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With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

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Analysis for Lossless Data Compression Algorithms for Low Bandwidth Networks

Cited by 3 publications

References 2 publications

Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression

Comparison of Huffman Algorithm and Lempel Ziv Welch Algorithm in Text File Compression

Enhanced Image Compression and Processing Scheme

Performance Analysis of Compression Algorithms on Matrix Data: Data Transfer Optimization in Microservices Architectures

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