Sparse Transform Matrix at Low Complexity for Color Image Compression
| International Journal of Computer Trends and Technology (IJCTT) | |
© - June Issue 2013 by IJCTT Journal | ||
Volume-4 Issue-6 | ||
Year of Publication : 2013 | ||
Authors :Dr. K. Kuppusamy, R.Mehala |
Dr. K. Kuppusamy, R.Mehala"Sparse Transform Matrix at Low Complexity for Color Image Compression"International Journal of Computer Trends and Technology (IJCTT),V4(6):1615-1620 June Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Image Processing is a powerful era of the Modern Digital Technology. Compression is a process of minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. In this paper, we have discusses about Digital Image Compression for the good performance complexity of still imagery and the comparative study of several algorithms. In future we are going to propose a new plan to provide a reduction in computation over the sparse matrix and using the various test images for the entropy coding and quality scalability is enabled by simply truncating the generated bit rate distortion performance.
References-
[1] Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1, pp 19-23, Feb-March 2001,
[2] Hong Zhang, Xiaofei Zhang & Shun Cao,“ Analysis & Evaluation of Some Image Compression Techniques,” High Performance Computing in Asia Pacific Region, 2000 Proceedings, 4th Int. Conference, vol. 2, pp 799-803,14-17 May, 2000
[3] Ming Yang & Nikolaos Bourbakis ,“An Overview of Lossless Digital Image Compression Techniques,” Circuits & Systems, 2005 48th Midwest Symposium, vol. 2 IEEE , pp 1099-1102, 7 – 10 Aug, 2005
[4]Milos Klima, Karel Fliegel,“Image Compression Techniques in the field of securityTechnology: Examples and Discussion,”Security Technology, 2004, 38th Annual 2004 Intn. Carnahan Conference, pp 278- 284,11-14 Oct., 2004
[5] Ismail Avcibas, Nasir Memon, Bulent Sankur, Khalid Sayood, “ A Progressive Lossless / Near Lossless Image Compression Algorithm,”IEEE Signal Processing Letters, vol. 9, No. 10, pp 312-314, October 2002.
[6] Dr. Charles F. Hall, “ A Hybrid Image Compression Technique,” Acoustics Speech & Signal Processing, IEEE International Conference on ICASSP’ 85, Vol. 10, pp 149- 152, Apr, 1985
[7] Wen Shiung Chen, en- HuiYang & Zhen Zhang, “ A New Efficient Image Compression Technique with Index- Matching Vector Quantization,” Consumer Electronics, IEEE Transactions, Vol. 43, Issue 2, pp 173- 182, May 1997.
[8] W.B.Pennebaker and J.L.Mitchell, “JPEG Still Image Compression Standard”, Chapman & Hall, New York, 1993.
[9] David H. Kil and Fances Bongjoo Shin, “ Reduced Dimension Image Compression And its Applications,”Image Processing, 1995, Proceedings, International Conference,Vol. 3 , pp 500-503, 23-26 Oct.,1995
[10] C.K. Li and H.Yuen, “A High Performance Image Compression Technique For Multimedia Applications,” IEEE Transactions on Consumer Electronics, Vol. 42, no. 2, pp 239-243, 2 May 1996.
Keywords —image compression, sparse matrix, entropy coding, quality scalability, bit rate etc.