Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Effect of Two Dimensional Image Compression on Statistical Features of Image using Wavelet Approach

Print
PDF
International Conference and Workshop on Emerging Trends in Technology
© 2011 by IJCA Journal
Number 3 - Article 1
Year of Publication: 2011
Authors:
Ashwani Kumar Dubey
Z A Jaffery
R P Singh

Ashwani Kumar Dubey, Z A Jaffery and R P Singh. Effect of Two Dimensional Image Compression on Statistical Features of Image using Wavelet Approach. IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (3):1-6, 2011. Full text available. BibTeX

@article{key:article,
	author = {Ashwani Kumar Dubey and Z A Jaffery and R P Singh},
	title = {Effect of Two Dimensional Image Compression on Statistical Features of Image using Wavelet Approach},
	journal = {IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET)},
	year = {2011},
	number = {3},
	pages = {1-6},
	note = {Full text available}
}

Abstract

The wavelet based approach becoming most common for image compressions and de-noising. The level of decomposition during image compression may be optimized to retain use full energy contents. In this paper we are analyzing the effect of image compressions on its statistical features. These statistical features will be utilized for image recognition and analysis. This analysis will help us in the designing of recognition techniques where image compression will be a prime requisite to save memory and channel space with enhanced speed. The real time image processing is the main application area of the proposed concept.

Reference

  • Al-Jawad, Naseer, and Jassim, Sabah 2010. Wavelet based Image Quality Self Measurements. In Proceeding of SPIE, Vol. 7708, 77080J, Florida, USA.
  • Chang, S. Grace, Yu, Bin and Vattereli, M. 2000. Adaptive Wavelet Thresholding for Image De-noising and Compression. IEEE Trans. Image Processing, 9(2000) 1532-1546.
  • Chui, C. K. 1992. Wavelets: A Tutorial in Theory and Applications. Academic Press.
  • Cormode, Graham and Garofalakis, Minos 2010. Histograms and Wavelets on Probabilistic Data. IEEE Transactions on Knowledge and Data Engineering, 22, 8(2010) 1142-1157.
  • Dappin, S. G., Manjunath, S. S., Rangarajan, L. and Shetty, S. S. (2009). Efficient Enhancement of Microarray Image Using Histogram Specification. In Proceeding of International Conference on Computer Technology and Development, ICCTD ’09. 1 (Kota Kinabalu, Malaysia, November 13-15, 2009). 247-251. DOI= http://doi.10.1109/ICCTD.2009.173
  • Kang, Jiayin, and Zhang, Wenjuan 2009. An Approach for Image Thresholding using CNN Associated with Histogram Analysis. In Proceeding of IEEE International Conference on Measuring Technology and Mechatronics Automation 2009. 1(Zhangjiajie, Hunan, April 11-12, 2009), IEEE Computer Society, Los Alamitos, CA, USA, 421- 424. DOI= http://doi.10.1109/ICMTMA.2009.311
  • Li, Jia, Wang, and James Z. 2003. Automatic Linguistic Indexing of Pictures by A Statistical Modeling Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25, 9, (2003) 1075-1088.
  • Luo, Taohua and He, Jian 2010. Fast Similarity Search with Blocking Wavelet-Histogram and Adaptive Particle Swarm Optimization. In Proceeding of Third International Conference on Knowledge Discovery and Data Mining. WKDD ’10. (Phuket, India, January 09-10, 2010). 334 – 337.DOI= http://doi.10.1109/WKDD.2010.26
  • Spampinato, C. 2009. Adaptive Objects Tracking by Using Statistical Features Shape Modeling and Histogram Analysis. In Proceeding of Seventh International Conference on Advances in Pattern Recognition, 2009. ICAPR '09. (Kolkata, India, February 0 4-06, 2009). 270 – 273. DOI= http://doi.10.1109/ICAPR.2009.106