The week's pick
Reseach Article
Compression of Noisy Images based on Sparsification using Discrete Rajan Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 132 - Number 12 |
| Year of Publication: 2015 |
| Authors: Kethepalli Mallikarjuna, Kodati Satya Prasad, M.V. Subramanyam |
10.5120/ijca2015907697
|
Kethepalli Mallikarjuna, Kodati Satya Prasad, M.V. Subramanyam . Compression of Noisy Images based on Sparsification using Discrete Rajan Transform. International Journal of Computer Applications. 132, 12 ( December 2015), 37-43. DOI=10.5120/ijca2015907697
Abstract
Image compression is usually carried out to reduce the amount of data required to store or communicate a digital image or video. The basic idea involved in the reduction process is removal of redundant data. Image compression exploits the fact that all images are not equally likely. In this regard a good number of Compression algorithms have been developed by researchers. As an alternative to the available traditional approaches, this paper presents the use of Discrete Rajan Transform for sparsification and image compression of noisy images. Discrete Rajan Transform is effective in introducing sparsity in images and thereby improving compressibility, the compromise being acceptable loss of data. In this paper, images with Gaussian, Poisson, Salt and pepper, and speckle noise have been investigated using the proposed method and a brief analysis is carried out in terms of perception of images as well in terms of three important parameters, Peak Signal-to-Noise Ratio, Mean Squared Error and Compression Ratio. On simulation, it was observed that DRT yielded higher quality image than the other candidate transforms used, namely Discrete Cosine Transform and Discrete Wavelet Transform.
References
- Zhu Gui bin, Cao Chang xiu, Hu Zhong yu, He Shi biao, Sen Bai, “An image scrambling and encryption algorithm based on affine transformation [J]”, Journal of Computer-Aided Design and Computer Graphics, Issue 6, pp. 711-715, 2003.
- En-hui Yang, Longji Wang, “Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table with Complete Baseline JPEG Decoder Compatibility [J]”, IEEE Trans. Image Processing, Vol. 18, no. 1, pp.63-74, 2009.
- Q Xia, X Li, L Zhou, K M Lam, ”Visual sensitivity-based low-bit-rate image compression algorithm[J]”, IEEE Trans. Image Processing, Vol. 6, no. 7, pp. 910-918, 2012.
- Zhiwei Xiong, Xiaoyan Su, Feng Wu, “Block-Based Image Compression with Parameter-Assistant Inpainting [J]”, IEEE Trans. image processing, Vol. 19, no. 6, pp. 1651-1657, 2010.
- Xingsong Hou, Guizhong Liu, and Yiyang Zou, “SAR Image Data Compression Using Wavelet Packet Transform and Universal-Trellis Coded Quantization”, IEEE Trans. Geoscience and Remote Sensing, Vol. 42, no. 11, pp. 2632-2641, November 2004.
- M. Zhang, G. Shao and K. Yi. “T-matrix and Its Applications in Image Processing [J]”, Electronics Letters, Vol. 40, no. 25, pp. 1583-1584, 2004.
- Huibin Chang, K. Ng Michael, and Tieyong Zeng, “Reducing Artifacts in JPEG Decompression Via a Learned Dictionary”, IEEE Trans. Signal Processing, vol. 62, no. 3, pp. 718-728 Feb. 2014.
- A. Skodras, C. Christopoulos, T. Ebrahimi, “The JPEG 2000 still-image compression standard”, Signal Processing Magazine, IEEE, Vol. 18, no. 5, pp. 36–58, 2001.
- D. J. Jackson, S. J. Hannah, "Comparative analysis of image compression techniques", IEEE Conf. System Theory, 1993. Proceedings SSST '93, Twenty-Fifth Southeastern Symposium on, pp. 513-517, Mar. 1993.
- Guangqi Shao, Yanping Wu, Yong A, Xiao Liu, Tiande Guo, “Fingerprint Compression Based on Sparse Representation”, IEEE Trans. Image Processing, vol. 23, no. 2, pp. 489-501 Feb. 2014.
- Tanaya Guha, Rabab K. Ward, “Image Similarity Using Sparse Representation and Compression Distance”, IEEE Trans. Multimedia, vol. 16, no. 4, pp. 980-987, June 2014.
- Jing-Ming Guo, Yun-Fu Liu, “Improved Block Truncation Coding Using Optimized Dot Diffusion”, IEEE Trans. Image Processing, vol.23, no. 3, pp. 1269-1275, March 2014.
- Chuan Qin, Chin-Chen Chang, Yi-Ping Chiu, “A Novel Joint Data-Hiding and Compression Scheme Based on SMVQ and Image Inpainting”, IEEE Trans. Image Processing, vol. 23, no. 3, pp. 969-978, March 2014.
- Shengli Chen, Xiaoxin Cheng, Jiapin Xu, "Research on image compression algorithm based on Rectangle Segmentation and storage with sparse matrix”, 9th International Conf. Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1904-1908, May 2012.
- Al-Shaykh, O.K.; Mersereau, R.M., "Lossy compression of noisy images", in Image Processing, IEEE Transactions on , vol.7, no.12, pp.1641-1652, Dec 1998.
- A. Macovski, Medical Imaging. Englewood Cliffs, NJ: Prentice-Hall, 1983.
- M. Rabbani, “Bayesian filtering of Poisson noise using local statistics”, IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 933–937, June 1988.
- W. Zeng, S. Daly, and S. Lei, “An overview of the visual optimization tools in JPEG 2000”, Signal Processing: Image Communication, vol. 17, no. 1, pp. 85–104, 2002.
- D. M. Chandler, M. A. Masry, and S. S. Hemami, “Quantifying the visual quality of wavelet-compressed images based on local contrast, visual masking, and global precedence”, in Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, pp. 1393–1397, November 2003.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity”, IEEE Transactions on Image Processing, vol. 13, no.4, pp. 600–612, 2004.
- K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti, and M. Carli, “New full-reference quality metrics based on HVS”, in Proceedings of the 2nd International Workshop on Video Processing and Quality Metrics, pp. 1–4, Scottsdale, Arizona, USA, 2006, CD-ROM.
- F. De Simone, D. Ticca, F. Dufaux, M. Ansorge, and T. Ebrahimi, “A comparative study of color image compression standards using perceptually driven quality metrics”, in Applications of Digital Image Processing XXXI, vol. 7073 of Proceedings of SPIE, San Diego, Calif, USA, 2008.
- N. Ponomarenko, F. Silvestri, K. Egiazarian, J. Astola, M. Carli, and V. Lukin, “On between-coefficient contrast masking of DCT basis functions”, in Proceedings of the 3rd International Workshop on Video Processing and Quality Metrics, pp. 1–4, Scottsdale, Ariz, USA, 2007, CD-ROM.
- G. K. Wallace, “The JPEG still picture compression standard”, Communications of the ACM, vol. 34, no. 4, pp. 30–44, 1991.
- D. Taubman and M. Marcellin,JPEG 2000: Image Compression Fundamentals, Standards and Practice, Kluwer, Boston, Mass, USA, 2002.
- Li Zhiqianga, Sun Xiaoxin, Du Changbin, Ding Qun, "JPEG Algorithm Analysis and Application in Image Compression Encryption of Digital Chaos", IEEE Conf. , pp. 185-189, Sept. 2013.
- Elad Michael, “Sparse and Redundant Representations : From Theory to Applications in Signal and Image Processing”, 2010, USA: Springer ISBN 978-1-4419-7011-4
- Ekambaram Naidu Mandalapu, Rajan E. G., 2009, “Rajan Transform and its Uses in Pattern Recognition”, Informatica 33, pp. 213-220.
- Kethepalli Mallikarjuna, Kodati Satya Prasad, and Makam Venkata Subramanyam, “Sparse Representation Based Image Compression Using Discrete Rajan Transform”, International Journal of Applied Engineering Research (IJAER), Vol. 10, No. 13, 2015, pp.33424-33429.
- Govindarajan Prashanthi, “Signal Sparsification with Discrete Rajan Transform (DRT): Principles, Properties and Applications", MSc, Staffordshire University, UK, 2012.
- Kethepalli Mallikarjuna, K.Satya Prasad, and M.V.Subramanyam, “Sparsification of Digital Images using Discrete Rajan Transform”, Journal of Information Processing Systems(JIPS), 2015, in press.
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