International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.11, Nov. 2018

An Improved Image Compression Algorithm Using Wavelet and Fractional Cosine Transforms

Full Text (PDF, 649KB), PP.19-27

Views:13   Downloads:0


Naveen kumar. R, B.N. Jagadale, J.S. Bhat

Index Terms

Discrete Wavelet Transform (DWT) decomposition;One-dimensional Discrete Fractional Cosine Transform (DFrCT);Quantization


The most significant parameters of image processing are image resolution and speed of processing.  Compressing the multimedia datasets, which are rich in quality and volume is challenging.  Wavelet based image compression techniques are the best tools for lossless image compression, however, they suffer by low compression ratio. Conversely fractional cosine transform based compression is a lossy compression technique with less image quality. In this paper, an improved compression technique is proposed by using wavelet transform and discrete fractional cosine transform to achieve high quality of reconstruction of an image at high compression rate. The algorithm uses wavelet transform to decompose image into frequency spectrum with low and high frequency sub bands. Application of quantization process for both sub bands at two levels increases the number of zeroes, however rich zeroes from high frequency sub bands are eliminated by creating the blocks and then storing only non-zero values and kill all blocks with zero values to form reduced array. The arithmetic coding method is used to encode the sub bands. The Experimental results of proposed method are compared with its primitive two dimensional fractional cosine and fractional Fourier compression algorithms and some significant improvements can be observed in peak signal to noise ratio and self-similarity index mode at high compression ratio.

Cite This Paper

Naveen kumar. R, B.N. Jagadale, J.S. Bhat, "An Improved Image Compression Algorithm Using Wavelet and Fractional Cosine Transforms", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.11, pp. 19-27, 2018.DOI: 10.5815/ijigsp.2018.11.03


[1]B.S.Manjunath, W.Y.Ma, "Texture Features for Browsing and Retrieval of image data”, IEEE Trans. on pattern analysis and machine intelligence, Vol.18, No 8, pp. 837-842, 1996.

[2]k. Sayood, Introduction to data compression, 3rd ed., academic press, Morgan Kaufmann Publishers, 2006

[3]W.B.Pennebaker, J.L. Mitchell, JPEG still image data compression standard, 1st ed., Kluwer Academic Publishers, 1992.

[4]T.L.B.yng, B.G.Lee, H.Yoo, “A low complexity lossless frame memory compression for display device”, IEEE Trans. on Consumer Electronics, Vol. 54, No.3, pp. 1453-1458, 2008.

[5]H. You, J. M. Jo, J. C. Jeong, “A hierarchical lossless  

image compression based on modified Hadamard transform”, in Proceedings of the 10th Workshop on Image processing and Understanding, pp. 305-310,1998.

[6]Jia Li, "An improved wavelet image lossless compression algorithm”, Journal of Optik, Vol.124, pp:1041-1044,2013.

[7]Macarena Boix, Begona canto, “Wavelet transform application to the compression of images”, Journal of Mathematical and computer modeling, Vol-52, pp.1265-1270, 2010.

[8]R.D. Dony, S. Haykin, “Optimally adaptive transform coding”, IEEE Trans. Image Processing , Vol.10, pp. 1358–1370,1995.

[9]A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989, p. 569.

[10]Tsai. M and Hung. H, K., “DCT and DWT Based Image Watermarking Using Sub-Sampling”, Proceeding of the Fourth International Conference on Machine Learning and Cybern, Guangzhou, pp. 5308-5313, 2005.

[11]J.W. Woods, S.D. O'Neil, “Subband coding of Images”, IEEE Trans. Acoustic Speech Signal Processing, ASSP-34, pp. 1278–1288,1986.

[12] vore, BjornJawerth, Bradly.J.Lucier, “Image compression through wavelet coding”, IEEE Trans. on information theory, Vol. 38, No. 2,pp.719-746, 1992.

[13]KUMAR, Naveen; JAGADALE, B. N.; BHAT, J. S.. “Improved Binary Tree Coding for Image Compression using Modified Singular Value Decomposition”. Journal of Informatics and Mathematical Sciences, [S.l.], v. 10, n. 1-2, p. 109 - 118, aug. 2018.

[14]C.Vijaya, J.S.Bhat, “Signal compression using discrete fractional Fourier transform and set partitioning in the hierarchical tree", Journal of Signal Processing, Vol. 86, pp.1976-1983.2008.

[15]Ranjeeth Kumar, A. Kumar, G. K.singh, “Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression”, Journal of Computer methods and programs in biomedicine, Vol. 129, pp. 135-148,2016.

[16]A. M. Rufai, Golamreza. A Hasndemirel, "Lossy image compression using singular value decomposition and Wavelet difference reduction”, Journal of Digital signal processing. Vol.24, pp.117-123, 2014.

[17]M.M.Siddeq, M.A.Rodrigues, "A Novel 2D Image compression algorithm based on two level DWT and DCT Transforms with an enhanced minimization-matrix-size algorithm for high resolution structured light 3D surface reconstruction", Journal of 3DR Express, Springer pub., pp:6-26.2015.

[18]Tim Bruylants, Adrian Munteanu, Peter Schelkens, "Wavelet-based volumetric medical image compression", Journal of Signal Processing: Image Communication, Vol.31, pp. 112-133.2015.

[19]A.S. Poulakidas, A. Srinivasan, O. E. gecioglu, O. Ibarra, T. Yang, “Image compression for fast wavelet-based sub-region retrieval", Journal Theoretical computer science, Vol. 240, pp:447-469,2000.

[20]Strang. G and Nguyen.T, Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA, 1996, HTTP: //www-math. mit. edu /gs/book/wfb.html.

[21]R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley Publishing Company, Reading, 2001.

[22]Z. Wang, “Fast algorithm for the discrete W transform and for the discrete Fourier transform,” IEEE Trans. Acoustic, Speech Signal Processing, vol. ASSP-32, pp. 803–816, 1984.

[23]Yeh, M.H. Pei, S.C, "A method for the discrete fractional Fourier transform computation”. IEEE Trans. Signal Process. Vol. 51, No. 3, pp. 889-891, 2003.

[24]Narsimha, M.J, Peterson, A.M, “On the computation of the discrete cosine transform”, IEEE Trans. communication, Vol. 26, No. 6, pp.934-936, 1978.

[25]Devid Solomon, Data compression, The complete reference, 4th ed. Springer Verlag London Limited-2007.

[26]Singh. K,(2006), “Performance of discrete fractional Fourier transform classes in signal processing applications (Doctoral Thesis), Retrieved from:dspace. /jspui/bitstream/123456789/94/1/P92233.pdf.