The limited available storage and bandwidth required for successful transmission of large images make image compression a key component in digital image transmission. Digital image application in various industries, such as entertainment and advertising, has brought image processing to the fore of these industries. However, the entire image processing is faced with the problem of data redundancy, which is mitigated through image compression. This is simply the art and science of reducing the number of bits/data of an image before it is transmitted and stored easily while the quality of image is maintained. Thus, through an exploratory study, this paper examines image compression as discussed in extant literature and emphasises on different methods used in image compression. The paper reviewed relevant literature from Elsevier, Emerald, IEEE, ProQuest and Google scholar databases. Specific methods are lossy and lossless techniques, which are further divided into run length encoding, and entropy encoding. In conclusion, the paper recommends compression techniques to adopt depending on the industry’s’ goals. Preferably, lossy compression is used to compress multimedia data which includes audio, video and images, while lossless compression technique is used to compress text and data files.
| Published in | Software Engineering (Volume 6, Issue 4) |
| DOI | 10.11648/j.se.20180604.12 |
| Page(s) | 116-120 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Image Compression, Lossy Technique, Lossless Technique, Transform Coding Encoder and Decoder
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APA Style
Ikerionwu Charles, Isonkobong Christopher Udousoro. (2019). The Application of Selective Image Compression Techniques. Software Engineering, 6(4), 116-120. https://doi.org/10.11648/j.se.20180604.12
ACS Style
Ikerionwu Charles; Isonkobong Christopher Udousoro. The Application of Selective Image Compression Techniques. Softw. Eng. 2019, 6(4), 116-120. doi: 10.11648/j.se.20180604.12
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Download@article{10.11648/j.se.20180604.12,
author = {Ikerionwu Charles and Isonkobong Christopher Udousoro},
title = {The Application of Selective Image Compression Techniques},
journal = {Software Engineering},
volume = {6},
number = {4},
pages = {116-120},
doi = {10.11648/j.se.20180604.12},
url = {https://doi.org/10.11648/j.se.20180604.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20180604.12},
abstract = {The limited available storage and bandwidth required for successful transmission of large images make image compression a key component in digital image transmission. Digital image application in various industries, such as entertainment and advertising, has brought image processing to the fore of these industries. However, the entire image processing is faced with the problem of data redundancy, which is mitigated through image compression. This is simply the art and science of reducing the number of bits/data of an image before it is transmitted and stored easily while the quality of image is maintained. Thus, through an exploratory study, this paper examines image compression as discussed in extant literature and emphasises on different methods used in image compression. The paper reviewed relevant literature from Elsevier, Emerald, IEEE, ProQuest and Google scholar databases. Specific methods are lossy and lossless techniques, which are further divided into run length encoding, and entropy encoding. In conclusion, the paper recommends compression techniques to adopt depending on the industry’s’ goals. Preferably, lossy compression is used to compress multimedia data which includes audio, video and images, while lossless compression technique is used to compress text and data files.},
year = {2019}
}
TY - JOUR T1 - The Application of Selective Image Compression Techniques AU - Ikerionwu Charles AU - Isonkobong Christopher Udousoro Y1 - 2019/01/16 PY - 2019 N1 - https://doi.org/10.11648/j.se.20180604.12 DO - 10.11648/j.se.20180604.12 T2 - Software Engineering JF - Software Engineering JO - Software Engineering SP - 116 EP - 120 PB - Science Publishing Group SN - 2376-8037 UR - https://doi.org/10.11648/j.se.20180604.12 AB - The limited available storage and bandwidth required for successful transmission of large images make image compression a key component in digital image transmission. Digital image application in various industries, such as entertainment and advertising, has brought image processing to the fore of these industries. However, the entire image processing is faced with the problem of data redundancy, which is mitigated through image compression. This is simply the art and science of reducing the number of bits/data of an image before it is transmitted and stored easily while the quality of image is maintained. Thus, through an exploratory study, this paper examines image compression as discussed in extant literature and emphasises on different methods used in image compression. The paper reviewed relevant literature from Elsevier, Emerald, IEEE, ProQuest and Google scholar databases. Specific methods are lossy and lossless techniques, which are further divided into run length encoding, and entropy encoding. In conclusion, the paper recommends compression techniques to adopt depending on the industry’s’ goals. Preferably, lossy compression is used to compress multimedia data which includes audio, video and images, while lossless compression technique is used to compress text and data files. VL - 6 IS - 4 ER -
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