Fast Singular value decomposition based image compression using butterfly particle swarm optimization technique (SVD-BPSO)
D.J. Ashpin Pabi, N.Puviarasan, P.Aruna, ,
Affiliations Research Scholar, Department of Computer Science and Engineering, Annamalai University, 608 002, India
:10.22362/ijcert/2017/v4/i4/xxxx [UNDER PROCESS]
Image compression is an important research area in an image processing system. Due to the compression of data rates, this finds crucial in applications of information security for the fast transmission. Singular Value Decomposition (SVD) is a compression technique which performs compression by using a smaller rank to approximate the original matrix of an image. SVD offers good PSNR values with low compression ratios. Compression using SVD for different singular values (Sv) with an acceptable PSNR increases the encoding time (ET). To minimize the encoding time, in this paper a fast compression technique SVD-BPSO is proposed using singular value decomposition and butterfly particle swarm optimization (BPSO). Application of the concept of BPSO towards singular value decomposition reduces the encoding time and improves the transmission speed. The performance of the proposed SVD-BPSO compression method is compared with SVD without optimization technique. The simulation results showed that the method achieves good PSNR with the minimum encoding time.
D.J. Ashpin Pabi et.al, “Fast Singular value decomposition based image compression using butterfly particle swarm optimization technique (SVD-BPSO)”, International Journal Of Computer Engineering In Research Trends, 4(4):128-135, April-2017.
1. Moonen M et.al,” Singular value decomposition updating algorithm for subspace tracking”, SIAM Journal on Matrix Analysis and Applications, 13(4):1015-38,October-1992.
2. Konda et.al, ”A new algorithm for singular value decomposition and its parallelization”, Parallel Comput., 35(6):331-344,June-2009.
3. Julie Kamm L, SVD-Based Methods for Signal and Image Restoration, PhD Thesis, 1998.
4. Yang J F et.al, “Combined Techniques of Singular Value Decomposition and Vector Quantization for Image Coding”, IEEE Trans. Image Processing, 4(8):1141 – 1146,August-1995.
5. Awwal Mohammed Rufai et.al, “Lossy Image Compression using singular value decomposition and wavelet difference reduction”, Digital signal Processing, 24:117- 123,January-2014.
6. Manoj Kumar, Ankita Vaish, An efficient encryption-then-compression technique for encrypted images using SVD, Digital Signal Processing, 60:81-89,January-2017.
7. Jin Wang, Zhensen Wu et.al, “An efficient spatial deblocking of images with DCT compression”, Digital Signal Processing, 42:80-88,July-2015.
8. Saiprasad Ravishankar, “Learning Sparsifying Transforms", IEEE Transactions on Signal Processing, 61(5):1072-1086,March-2013.
9. K.M.M. Prabhu et.al, “3-D warped discrete cosine transform for MRI image compression”, Biomedical Signal Processing and Control, 8(1):50-58,January-2013.
10. Kaveh Ahmadi et.al, “An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization”, Signal Processing: Image Communication, 32:33-39,March-2015.
11. Fouzi Douak et.al, “Color image compression algorithm based on the DCT transform combined to an adaptive block scanning”, Int. J. Electron. Commun. (AEU), 65(1):16-26,January-2011.
12. Lige Wang et.al, “Interpretation of Particle Breakage under Compression using X-ray”, Computed Tomography and Digital Image Correlation, Procedia Engineering, 102:240 – 248, 2015.
13. Milan S. Savic et.al, “Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization”, Expert Systems with Applications, 42(21):7285–7291,November-2015.
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