International Journal of Data Science and Analysis
Volume 3, Issue 1, February 2017, Pages: 1-4
Received: Oct. 19, 2016;
Accepted: Nov. 24, 2016;
Published: Mar. 21, 2017
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Ruchita K. Ingole, Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering & Management, Amravati, Maharashtra, India
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.
Ruchita K. Ingole,
Embedded Image Compression: A Review, International Journal of Data Science and Analysis.
Vol. 3, No. 1,
2017, pp. 1-4.
Julia Minguillon, Jordi Herrera-Joancomart, Joan Serra-Sagrista and Fernando Garcıa-Vılchez, “Influence of Mark Embedding Strategies on Lossless Compression of Ultraspectral Images”, IEEE, vol. 4, issue 5, 2005.
I. Aouadi and O. Hammami, “Low Power JPEG-2000 Image Compression for Industrial Embedded Applications”, IEEE International Conference on industrial Technology (ICIT), 2004.
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.
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.
Shaowei Li and Xinqing Zhuang, “Embedded Based Radar Image Acquisition and Compression”, International Conference on Electronic & Mechanical Engineering and Information Technology, 2011.
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.
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.
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.
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.
Pavel Morozkin, Marc Swynghedauw and Maria Trocan, “An Image Compression for Embedded Eye-Tracking Applications”, vol. 4, issue 16, Journal of European Union, 2016.
Ranjan Kumar Senapati, Umesh C. Pati and Kamala Kanta Mahapatra, “Reduced memory, low complexity embedded image compression algorithm using hierarchical listless discrete Tchebichef transform”, journal of IET Image Process., Vol. 8, Iss. 4, pp. 213–238, 2014.
Han Sae Song and Nam Ik Cho, “DCT-Based Embedded Image Compression With a New Coefficient Sorting Method”, IEEE Signal Processing Letters, Vol. 16, No. 5, May 2009.
Srikanth and Sukadev Meher, “Compression Efficiency for Combining Different Embedded Image Compression Techniques with Huffman Encoding”, International conference on Communication and Signal Processing, April 3-5, India, 2013.
Li Zhu and Yi min Yang, “Embeded Image Compression Using Differential Coding and Optimization Method”, IEEE, issue 11, 2011.
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.
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.
Rehna V. J, and Jeya Kumar M. K, “Wavelet Based Image Coding Schemes: A Recent Survey”, International Journal on Soft Computing (IJSC) Vol. 3, No. 3, August 2012.
Pardeep Singh, Sugandha Sharma and Bhupinder Singh, “Comparative analysis of Embedded Zero tree and Fractal Image Compression Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 2, February 2012.