Fast Singular value decomposition based image compression using butterfly particle swarm optimization technique (SVD-BPSO)
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Abstract
Image compression is an essential research area in image processing systems. Due to the need for data rate compression, it plays a crucial role in applications related to information security and fast transmission. Singular Value Decomposition (SVD) is a compression technique that approximates the original image matrix using a smaller rank. SVD offers good PSNR values with low compression ratios. However, compression using SVD for different singular values (Sv) with an acceptable PSNR increases the encoding time (ET). To minimize the encoding time, this paper proposes a fast compression technique called SVD-BPSO, which utilizes singular value decomposition and butterfly particle swarm optimization (BPSO). By applying the concept of BPSO to singular value decomposition, the proposed method reduces the encoding time and improves the transmission speed. The performance of the proposed SVD-BPSO compression method is compared with SVD without optimization techniques. The simulation results show that the method achieves good PSNR with the minimum encoding time.
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References
Moonen, M., et al. (1992). Singular value decomposition updating algorithm for subspace tracking. SIAM Journal on Matrix Analysis and Applications, 13(4), 1015-1038.
Konda, et al. (2009). A new algorithm for singular value decomposition and its parallelization. Parallel Computing, 35(6), 331-344.
Julie Kamm, L. (1998). SVD-Based Methods for Signal and Image Restoration. PhD Thesis.
Yang, J. F., et al. (1995). Combined Techniques of Singular Value Decomposition and Vector Quantization for Image Coding. IEEE Transactions on Image Processing, 4(8), 1141-1146.
Awwal Mohammed Rufai, et al. (2014). Lossy Image Compression using singular value decomposition and wavelet difference reduction. Digital Signal Processing, 24, 117-123.
Manoj Kumar, Ankita Vaish. (2017). An efficient encryption-then-compression technique for encrypted images using SVD. Digital Signal Processing, 60, 81-89.
Jin Wang, Zhensen Wu, et al. (2015). An efficient spatial deblocking of images with DCT compression. Digital Signal Processing, 42, 80-88.
Saiprasad Ravishankar. (2013). Learning Sparsifying Transforms. IEEE Transactions on Signal Processing, 61(5), 1072-1086.
K.M.M. Prabhu, et al. (2013). 3-D warped discrete cosine transform for MRI image compression. Biomedical Signal Processing and Control, 8(1), 50-58.
Kaveh Ahmadi, et al. (2015). An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization. Signal Processing: Image Communication, 32, 33-39.
Fouzi Douak, et al. (2011). Color image compression algorithm based on the DCT transform combined to an adaptive block scanning. Int. J. Electron. Commun. (AEU), 65(1), 16-26.
Lige Wang, et al. (2015). Interpretation of Particle Breakage under Compression using X-ray, Computed Tomography and Digital Image Correlation. Procedia Engineering, 102, 240-248.
Milan S. Savic, et al. (2015). Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization. Expert Systems with Applications, 42(21), 7285-7291.