Neeru JINDAL, Kulbir SINGH, Kritika MITTAL

Image compression algorithm with reduced blocking artifacts

The modern communication era has led to a proliferation of digital media contents. However, the large volume of data poses difficulties because of increased bandwidth and limited storage space. Hence, this has led to the need for compression techniques. Image compression with block processing allows the coder to adapt to local image statistics and exploit the correlation present among neighboring image pixels. The main degradation factor of block transform coding is blocking artifacts (visually undesirable patterns) at high compression ratios. The degradation occurs because of coarse quantization of the transform coefficients and the independent processing of the blocks. In this paper, the novelty of the algorithm is its ability to detect and reduce the blocking artifacts using nonseparable discrete fractional Fourier transform (NSDFrFT) at high compression ratios. Three transform techniques, namely nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, were implemented. The NSDFrFT-bicubic interpolation resulted in a structurally similar high subjective quality reconstructed image with reduced blocking (for low frequency images) at high compression ratios. Simulation results are calculated with many image quality metrics such as peak signal to noise ratio, mean square error, structural similarity index, and gradient magnitude similarity measure. Evaluations, such as comparisons between the proposed and existing algorithms (DFrFT, FFT), are presented with relevant tables, graphs, and figures

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[1] McIntyre KA. Dynamic Bandwidth Adaptive Image Compression/Decompression Scheme. U.S. Patent 7 024 045, 2006.
[2] Yng TLB, Lee BG, Yoo H. A low complexity and lossless frame memory compression for display devices. IEEE T Consum Electr 2008; 54: 1453-1458.
[3] Shukla J, Alwani M, Tiwari AK. A survey on lossless image compression methods. In: IEEE 2010 International Conference on Computer Engineering and Technology; 1618 April 2010; Chengdu, China. New York, NY, USA: IEEE. pp. V6-136-V6-141.
[4] Grgic S, Grgic M, Zovko-Cihlar B. Performance analysis of image compression using wavelets. IEEE T Ind Electron 2001; 48: 682-695.
[5] Wiener N. Hermitian polynomials and Fourier analysis. J Math Phys 1929; 8: 70-73.
[6] Gonzalez RC, Woods RE. Digital Image Processing. 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2008.
[7] Jayaraman S, Esakkirajan S, Veerakumar T. Digital Image Processing. New Delhi, India: Tata McGraw-Hill Education, 2011.
[8] Bandyopadhyay SK, Paul TU, Rajchoudhury A. Image compression using approximate matching and run length. Int J Adv Comp Sci Appl 2011; 2: 117-121.
[9] Murugan G, Kannan E, Arun S. Lossless image compression algorithm for transmitting over low bandwidth line. Int J Comp Sci Inf Secur 2011; 9: 140-145.
[10] Thyagarajan KS. Still Image and Video Compression with MATLAB. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011.
[11] Skodras A, Christopoulos C, Ebrahimi T. The JPEG 2000 still image compression standard. IEEE T Signal Proces 2001; 18: 36-58.
[12] Watson AB. Image compression using the discrete cosine transform. Math J 1994; 4: 81-88.
[13] Jindal N, Singh K. Image and video processing using discrete fractional transforms. Sign Imag Vid Process 2014; 8: 1543-1553.
[14] Singh K. Performance of discrete fractional Fourier transform classes in signal processing applications. PhD, Thapar University, Patiala, India, 2005.
[15] Ozaktus HM, Zalevsky Z, Kutay MA. The fractional Fourier transform with applications in optics and signal processing. New York, NY, USA: John Wiley & Sons Ltd., 2000.
[16] Ludwig LF. Correction of Image Misfocus via Fractional Fourier Transform. U.S. Patent 6 687 418, 2004.
[17] Cusmario A. Cryptography Method using Modified Fractional Fourier Transform Kernel. U.S. Patent 6 718 038, 2004.
[18] Namias V. The fractional order Fourier transform and its applications to quantum mechanics. IMA J Appl Math 1980; 25: 241-265.
[19] Ozaktus HM, Mendlovic D. Fractional Fourier optics. J Opt Soc Am 1995; 12: 743-751.
[20] Cariolario G, Ersrghe T, Kraniauskas P, Laurenti N. A unified framework for the fractional Fourier transform. IEEE T Signal Proces 1998; 46: 3206-3212.
[21] Pei SC, Yeh MH, Luo TL. Fractional Fourier series expansion for finite signals and dual extension to discrete-time fractional Fourier transform. IEEE T Signal Proces 1999; 47: 2883-2888.
[22] Pei SC, Yeh MH. Discrete fractional Fourier transform. In: IEEE 1996 International Symposium on Connecting the World; 1215 May 1996; Atlanta, GA. New York, NY, USA: IEEE. pp. 536-539.
[23] Santhanam B, McClellan JH. The DRFT-a rotation in time-frequency space. In: IEEE 1995 International Conference on Acoustics, Speech, and Signal Processing; 912 May 1995; Detroit, MI, USA. New York, NY, USA: IEEE. pp. 921-924.
[24] Pei SC, Ding JJ. Closed-form discrete fractional and affine Fourier transforms. IEEE T Signal Proces 2000; 48: 1338-1353.
[25] Candan C, Kutay MA, Ozaktus HM. The discrete fractional Fourier transform. IEEE T Signal Proces 2000; 48: 1329-1338.
[26] Singh AK, Saxena R. DFRFT: a classified review of recent methods with its application. J Eng 2013; 2013: 1-13.
[27] Ozaktas HM, Ankan O, Kutay MA, Bozdagi G. Digital computation of the fractional Fourier transform. IEEE T Signal Proces 1996; 44: 2141-2150.
[28] Pei SC, Yeh MH, Tseng CC. Discrete fractional Fourier transform based on orthogonal projections. IEEE T Signal Proces 1999; 47: 1335-1348.
[29] Pei SC, Hsue WL. Random discrete fractional Fourier transform. IEEE Signal Proc Let 2009; 16: 1015-1018.
[30] Yeh MH, Pei SC. A method for the discrete fractional Fourier transform computation. IEEE T Signal Proces 2003; 51: 889-891.
[31] Ludwig LF. High-Accuracy Centered Fractional Fourier Transform Kernel for Optical Imaging and Other Applications. U.S. Patent 8 712 185, 2014.
[32] Ludwig LF. Discrete Fractional Fourier Numerical Environments for Computer Modeling of Image Propagation Through a Physical Medium in Restoration and Other Applications. U.S. Patent 8 094 969, 2012.
[33] Ding JJ, Pei SC. Heisenbergs uncertainty principles for the 2-D nonseparable linear canonical transforms. Signal Process 2013; 93: 1027-1043.
[34] Sahin A, Kutay MA, Ozaktus HM. Nonseparable two-dimensional fractional Fourier transform. Appl Optics 1998; 37: 5444-5453.
[35] Prajapati A, Naik S, Mehta S. Evaluation of different image interpolation algorithms. Int J Comp Appl 2012; 58: 6-12.
[36] Richardson IEG. H.264 and MPEG-4 Video Compression. West Sussex, UK: John Wiley & Sons, Inc., 2003.
[37] Whang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE T Image Process 2004; 13: 600-612.
[38] Xue W, Zhang L, Mou X, Bovik AC. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE T Image Process 2014; 23: 684-695.
[39] Singh J, Singh S, Singh D, Uddin M. Detection method and filters for blocking effect reduction of highly compressed images. Signal Process Image Comm 2011; 26: 496-503.
[40] Jindal N. Performance of fractional transforms in image and video processing. PhD, Thapar University, Patiala, India, 2014.
[41] Hu J, Deng J, Wu J. Image compression based on improved FFT algorithm. J Netw 2011; 6: 1041-1048.
[42] Rout S. Orthogonal vs. biorthogonal wavelets for image compression. MSc, Virginia Tech, Blacksburg, VA, USA, 2003.