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In this paper we are presenting a three-stage near lossless image compression scheme. It belongs to the class of lossless coding which consists of wavelet based decomposition followed by modified duplicate free run-length coding. We go for the selection of optimum bit rate to guarantee minimum MSE (mean square error), high PSNR (peak signal to noise ratio) and also ensure that time required for computation is very less unlike other compression schemes. Hence we propose ‘A wavelet based novel approach for near lossless image compression’. Which is very much useful for real time applications and is also compared with EZW, SPIHT, SOFM and the proposed method is out performed.
Słowa kluczowe
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Tom
Strony
51--57
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
- Department of Electronics and Communication Engineering, Parvathareddy Babulu Reddy Visvodaya Institute of Technology & Science, Kavali, India
autor
- Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, India
Bibliografia
- [1] Villasenor, J., Belzer, B., Liao, J.: Wavelet Filter Evaluation for Image Compression. IEEE Transactions on Image Processing, Vol. 2, pp. 1053-1060, August 1995.
- [2] Arora, R. et al: An Algorithm for Image Compression Using 2D Wavelet Transform. International Journal of Engineering Science and Technology (IJEST), Vol. 3, No. 4, Apr 2011.
- [3] Anitha Sheela, K., Sreenivasulu, P., Asha Rani, M.: Neural Networks and Lifting Scheme based Image Compression. World Academy of Science, Engineering and Technology 69, 2010.
- [4] Raja, S. P., Suruliandi, A.: Analysis Of Efficient Wavelet based Image Compression Techniques. 2010 Second International conference on Computing, Communication and Networking Technologies.
- [5] Xiao, W., Liu, H.: Using Wavelet Networks in Image Compression. 2011 Seventh International Conference on Natural Computation.
- [6] Walker, J. S.: A Primer on Wavelets and Their Scientific Applications, Second edition, Taylor & Francis Group, LLC, Beijing, Jun 2008.
- [7] Peng, X., Xu, J., Wu, F.: Directional Filtering Transform for Image/Intra-Frame Compression. IEEE Transactions On Image Processing, Vol. 19, No. 11, November 2010.
- [8] Ke, L., Marcellin, M.: Near-lossles image compression: Minimum entropy, constrained-error DPCM. IEEE Trans. Image Process., vol.7, no. 2, pp. 225–228, Feb. 1998.
- [9] Reichel, J., Menegaz, G., Nadenau, M. J., Kunt, M.: Integer wavelet transform for embedded lossy to lossless image compression. IEEE Trans. Image Process., vol. 10, no. 3, pp. 383–392, Mar. 2001.
- [10] Adams, M. D., Kossentini, F.: Reversible integer-to-integer wavelet transforms for image compression: Performance evaluation and analysis. IEEE Trans. Image Process., vol. 9, no. 6, pp. 1010–1024, Jun.2000.
- [11] Yea, S., Pearlman, W. A.: A wavelet-based two-stage near-lossless coder. IEEE transactions on image processing, vol. 15, no. 11, November 2006.
- [12] Al-Wahaib, M. S., Wong K.-S.: A Lossless Image Compression Algorithm Using Duplication Free Run-Length Coding. 2010 Second International Conference on Network Applications, Protocols and Services.
- [13] Chen, K., Ramabadran, T.: Near-lossless compression of medical images through entropycoded DPCM. IEEE Trans. Med. Imag., vol. 13, no. 9, pp. 538–548, Sep. 1994.
- [14] Marpe, D., Blattermann, G., Ricke, J., Maab, P.: A two-layered wavelet-based algorithm for efficient lossless and lossy image compression. IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 10, pp. 1094–1102, Oct. 2000.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-27f06d05-6b17-430a-a7e6-44b5d92752dd
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