Image Compression Techniques Using Linear Algebra with SVD Algorithm
Keywords:Image Compression, Singular Value Decomposition, MSE, Lossy Image Compression, PSNR
In recent days, the data are transformed in the form of multimedia data such as images, graphics, audio and video. Multimedia data require a huge amount of storage capacity and transmission bandwidth. Consequently, data compression is used for reducing the data redundancy and serves more storage of data. In this paper, addresses the problem (demerits) of the lossy compression of images. This proposed method is deals on SVD Power Method that overcomes the demerits of Python SVD function. In our experimental result shows superiority of proposed compression method over those of Python SVD function and some various compression techniques. In addition, the proposed method also provides different degrees of error flexibility, which give minimum of execution of time and a better image compression.
A. J. Madhuri, “Digital Image Processing. An Algorithmic Approach,” pp. 175–217. PHI, New Delhi, 2006.
M. J. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS,” IEEE Transactions Image Processing, Vol. 2, pp. 1309–1324, 2000.
M. A. Alkhalayleh and A. M. Otair, “A new lossless method of image compression by decomposing the tree of Huffman technique,” International journal of imaging & robotics Vol. 15, No. 2, pp. 79–96, 2015.
W. Jianji, Z. Nanning, L. Yuehu and Z. Gang,”Parameter analysis of fractal image compression and its applications in image sharpening and smoothing,” Signal Processing: Image Communication journal, Vol. 28, pp. 681– 687, 2013.
A. Bilgin, W. Michael, M. Marcellin, I. Altbach, “Compression of electrocardiogram signal using JPEG2000,” IEEE Transactions on Communications Electronics (ICIP), Vol. 49, No. 4, pp. 833–840, 2003.
M. R. Awwal, G. Anbarjafari, H. Demirel, “Lossy image compression using singular value decomposition and wavelet difference reduction,” Digital Signal Process, Vol. 24, pp. 117–123, 2014.
M. Adiwijaya, B. K. Dewi, F. A. Yulianto and B. Purnama, “Digital image compression using graph coloring quantization based on wavelet SVD,” Journal of Physics Conference Series, Vol. 423, No. 1, pp. 012-019, 2013.
A. Ranade, S. S. Mahabalarao and S. Kale, “A variation on SVD based image compression,” Image and Vision Computing journal, Vol. 25, No. 6, pp. 771–777, 2007.
M. Doaa and A. F. Chadi, “Image compression using block truncation coding,” Cyber J. Multidiscip. J. Sci. Technol. J. Sel. Areas Telecommun. (JSAT), February, 2011.
E. J. Delp and O.R. Mitchell, “Image compression using block compression”, IEEE Transactions onCommunications Vol. 27, No. 9, pp. 1335–1342, 1979.
C. C. Tsou, S. H. Wu and Y. C. Hu, “Fast pixel grouping technique for block truncation coding,” In: Workshop on Consumer Electronics and Signal Processing (WCEsp05), Yunlin, pp. 17–18, Nov. 2005.
N. K. El Abbadi, A. Al Rammahi, D. S. Redha and M. Abdul-Hameed, “Image compression based on SVD and MPQ-BTC”, Journal 0f Computer Science, Vol. 10, No. 10, pp. 2095–2104, 2014.
K. El Asnaoui and Y. Chawki, “Two new methods for image compression,” International journal of imaging & robotics, Vol. 15, No. 4, pp. 1–11, 2015.
A. H. Bentbib and A. Kanber, “Block power method for SVD decomposition,” Analele Stiintifice ale Universitatii Ovidius Constanta Seria Matematic, Vol. 23, No. 2, pp. 45–58, 2015.