Embedded Image Compression: A Review

Ruchita K. Ingole

doi:doi:10.11648/j.ijdsa.20170301.11

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Embedded Image Compression: A Review

Ruchita K. Ingole

Published in International Journal of Data Science and Analysis (Volume 3, Issue 1)

Received: 19 October 2016 Accepted: 24 November 2016 Published: 21 March 2017

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Abstract

In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.

Published in	International Journal of Data Science and Analysis (Volume 3, Issue 1)
DOI	10.11648/j.ijdsa.20170301.11
Page(s)	1-4
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), 2024. Published by Science Publishing Group

Keywords

Embedded Image, Wavelet Transform, Image Coder, Compression Standard and Redundancy

References

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[2]	I. Aouadi and O. Hammami, “Low Power JPEG-2000 Image Compression for Industrial Embedded Applications”, IEEE International Conference on industrial Technology (ICIT), 2004.
[3]	Li Guoli, Zhang Jian, Wang Qunjing, Hu Cungang, Deng Na And Li Jianping, “Application of Region Selective Embedded Zerotree Wavelet Coder in CT Image Compression”, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
[4]	Oguzhan Urhan and Sarp Ertürk, “Parameter Embedding Mode and Optimal Post-Process Filtering for Improved WDCT Image Compression”, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 18, No. 4, April 2008.
[5]	Shaowei Li and Xinqing Zhuang, “Embedded Based Radar Image Acquisition and Compression”, International Conference on Electronic & Mechanical Engineering and Information Technology, 2011.
[6]	Khamees Khalaf Hasan and Umi Kalthum Ngah, “The Most Proper Wavelet Filters in Low-Complexity and an Embedded Hierarchical Image Compression Structures for Wireless Sensor Network Implementation Requirements”, IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, 23-25 Nov. 2012.
[7]	Nicola Cottini, Leonardo Gasparini, Marco De Nicola, Nicola Massari and Massimo Gottardi, “A CMOS Ultra-Low Power Vision Sensor With Image Compression and Embedded Event-Driven Energy-Management”, IEEE Journal On Emerging And Selected Topics In Circuits And Systems, Vol. 1, No. 3, September 2011.
[8]	Ali A. Al-hamid, Ahmed Yahya and Reda A. El-Khoribi, “Optimized Image Compression Techniques for the Embedded Processors”, International Journal of Hybrid Information Technology Vol. 9, No. 1, pp. 319-328, 2016.
[9]	Mengyun Yue, Dong Wu and Zheyao Wang, “A 15-bit Two-Step Sigma-Delta ADC with Embedded Compression for Image Sensor Array”, IEEE, vol. 3, issue 13, 2013.
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[15]	Ali A. Al-hamid, Ahmed Yahya and Reda A. El-Khoribi, “Optimized Image Compression Techniques for the Embedded Processors”, International Journal of Hybrid Information Technology, Vol. 9, No. 1, pp. 319-328, 2016.
[16]	Yasaswi Velamuri, Sandhya Patnayakuni and Nancharaiah Vejendla, “Compression Efficiency of Different Embedded Image Compression Techniques with Huffman Encoding”, International Journal of Engineering Research and Development, Volume 9, Issue 12, PP. 13-20, February 2014.
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Ruchita K. Ingole. (2017). Embedded Image Compression: A Review. International Journal of Data Science and Analysis, 3(1), 1-4. https://doi.org/10.11648/j.ijdsa.20170301.11

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Ruchita K. Ingole. Embedded Image Compression: A Review. Int. J. Data Sci. Anal. 2017, 3(1), 1-4. doi: 10.11648/j.ijdsa.20170301.11

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AMA Style

Ruchita K. Ingole. Embedded Image Compression: A Review. Int J Data Sci Anal. 2017;3(1):1-4. doi: 10.11648/j.ijdsa.20170301.11

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@article{10.11648/j.ijdsa.20170301.11,
  author = {Ruchita K. Ingole},
  title = {Embedded Image Compression: A Review},
  journal = {International Journal of Data Science and Analysis},
  volume = {3},
  number = {1},
  pages = {1-4},
  doi = {10.11648/j.ijdsa.20170301.11},
  url = {https://doi.org/10.11648/j.ijdsa.20170301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170301.11},
  abstract = {In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.},
 year = {2017}
}

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TY - JOUR
T1 - Embedded Image Compression: A Review
AU - Ruchita K. Ingole
Y1 - 2017/03/21
PY - 2017
N1 - https://doi.org/10.11648/j.ijdsa.20170301.11
DO - 10.11648/j.ijdsa.20170301.11
T2 - International Journal of Data Science and Analysis
JF - International Journal of Data Science and Analysis
JO - International Journal of Data Science and Analysis
SP - 1
EP - 4
PB - Science Publishing Group
SN - 2575-1891
UR - https://doi.org/10.11648/j.ijdsa.20170301.11
AB - In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.
VL - 3
IS - 1
ER -

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Author Information

Ruchita K. Ingole

Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering & Management, Amravati, Maharashtra, India

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APA Style

Ruchita K. Ingole. (2017). Embedded Image Compression: A Review. International Journal of Data Science and Analysis, 3(1), 1-4. https://doi.org/10.11648/j.ijdsa.20170301.11

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ACS Style

Ruchita K. Ingole. Embedded Image Compression: A Review. Int. J. Data Sci. Anal. 2017, 3(1), 1-4. doi: 10.11648/j.ijdsa.20170301.11

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AMA Style

Ruchita K. Ingole. Embedded Image Compression: A Review. Int J Data Sci Anal. 2017;3(1):1-4. doi: 10.11648/j.ijdsa.20170301.11

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@article{10.11648/j.ijdsa.20170301.11,
  author = {Ruchita K. Ingole},
  title = {Embedded Image Compression: A Review},
  journal = {International Journal of Data Science and Analysis},
  volume = {3},
  number = {1},
  pages = {1-4},
  doi = {10.11648/j.ijdsa.20170301.11},
  url = {https://doi.org/10.11648/j.ijdsa.20170301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170301.11},
  abstract = {In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.},
 year = {2017}
}

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