Lossy Compression of Color Images using Lifting Scheme and Prediction Errors

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Author(s)

Manoj Kumar 1,* Ankita Vaish 1

1. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2016.04.01

Received: 6 Dec. 2015 / Revised: 2 Jan. 2016 / Accepted: 23 Feb. 2016 / Published: 8 Apr. 2016

Index Terms

Compression Ratio, Huffman Coding, Image Compression, Integer Wavelet Transform, Peak-Signal-to-Noise Ratio

Abstract

This paper presents an effective compression technique for lossy compression of color images. After reducing the correlation among R, G and B planes using YCoCg-R transform, the Integer Wavelet Transform (IWT) is applied on each of the transformed planes independently up to a desired level. IWT decomposes the input image into an approximation and several detail subbands. Approximation subband is compressed losslessly using prediction errors and Huffman coding, while each of the detail subbands are compressed independently using an effective quantization and Huffman coding. To show the effectiveness of proposed scheme, it is compared with several existing schemes and a state of art for image compression JPEG2000 and it is observed that the proposed scheme outperforms over the existing techniques and JPEG2000 with less degradation in the quality of reconstructed images while achieving high compression performance.

Cite This Paper

Manoj Kumar, Ankita Vaish, "Lossy Compression of Color Images using Lifting Scheme and Prediction Errors", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.4, pp.1-8, 2016. DOI:10.5815/ijmecs.2016.04.01

Reference

[1]W. B. Pennebaker and J. L. Mitchell, “JPEG Still Image Data Compression Standard”, Van Nostrand Reinhold, 1993.
[2]“Information Technology- Lossless and Near-Lossless Compression of Continuous-Tone Still Images (JPEG-LS)”, 1993.
[3]M. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS”, IEEE Trans. Image Process, Vol. 9, 1309–1324, 2000.
[4]X. Wu and N. Memon, “Context-based adaptive loss-less image coding”, IEEE Trans. Commun., 437-444, 1997.
[5]ISO/IEC and ITU-T, “Information Technology JPEG2000 image coding system: core coding sys-tem”, ISO/IEC International Standard 15444-1 and ITU-T recommendation, 2004.
[6]ITU-T and ISO/IEC, “JPEG XR image coding system- Part 1:Core Coding System”, Image coding Specification, 2011.
[7]E.J. Delp and O.R. /mitchell, “Image compresion us-ing block truncation”, IEEE Transactions on Commu-nications, vol. 27, 1335-1342, 1979.
[8]T. Kurita and N. Otsu, “A method of block truncation coding for color image compression”, IEEE Transac-tions on Communications, vol. 41, 1270-1274, 1943.
[9]Y. Wu and D.C. Coll, “Single bit map block trunca-tion coding for color image”, IEEE Transactions on Communications, vol. 35, 1987.
[10]S.C. Tai, Y.C. Lin and J.F. Lin, “Single bitmap block truncation coding of color image using hopfield neu-ral network”, Inform. Sci., 103, 211-218, 1997.
[11]B. Dhara and B. Chanda, “Color image compression based on block truncation coding using pattern fitting principle”, Pattern Recognition, vol. 40, 2408-2417, , 2007.
[12]C. Yang, J. Lin and W. Tsai, “Color image com-pression by moment-preserving and block trunca-tion coding techniques”, IEEE Transactions on Communications, vol. 45, 1513-1516, 1997.
[13]S.I. Olsen, “Block truncation and planar image coding”, Pattern recognition letter, vol. 21, 1141-1148, 2000.
[14]F. Douak, R. Berizid and N. Benoudjit, “Color image compression algorithm based on the DCT transform combined to an adaptive block scanning”, Int. J. Elec-tron. Commun, vol. 65, 16–26, 2011.
[15]Y. Feng and N. Nasrabadi, “Dynamic address-vector quantisation of RGB colour images”, IEE Proceed-ings Communications Speech and Vision, vol. 238, 225-231, 1991.
[16]W. Lee and C. Chan, “Dynamic finite state VQ of colour images using stochastic learning”, Signal Pro-cessing: Image Communication, vol. 6, 1–11, 1994.
[17]S. Makrogiannis, G. Economou and S. Fotopoulos, “Region oriented compression of color images using fuzzy inference and fast merging”, Pattern Recognition, vol. 35, 1807–1820, 2002.
[18]A. Alkholidi, A. Alfalou and H. Hamam, “A new ap-proach for optical colored image compression using the jpeg standards”, Signal Processing, vol. 87, 569–5839, 2007.
[19]D. Chikouche, R.Benzid and M. Bentoumi, “Application of the dct and arithmetic coding to medical image compression”, International conference on in-formation and communication technologiess: from theory to applications (ICTTA), 1-5, 2008.
[20]W. Yu and F. Sun,J. Fritts, “Efficient rate control for jpeg2000”, IEEE Transactions on Circuits and Sys-tems for Video Technology, vol. 16, 577-589, 2006.
[21]A. Skodras, C. Christopoulos and T. Ebrahimi, “Jpeg2000: the upcoming still image compression standard”, Pattern Recognition Letters, vol. 22, 1337-1345, 2001.
[22]W. Pearlman, A. Islam, N. Nagaraj and A. Said, “Efficient, low-complexity image coding with a set-partitioning embedded block coder”, IEEE Transac-tions on Circuits and Systems for Video Technology, vol. 14, 1219-1235, 2004.
[23]A. badpour and S. Kasaei, “Color PCA eigen images and their application to compression and watermarking”, Image and Vision Computing, ol. 26, 878-890, 2008.
[24]J. M. Shapiro, “Embedded image coding using ze-rotrees of wavelet coefficients”, IEEE Trans. Signal Process., vol. 41(12), 3445-3462, 1993.
[25]A. Said and W. A. Pearlman, “A new, fast and efficient image codec based on set partitioning in hi-erarchical trees”, IEEE Transactions on Circuit and System for Video Technology, vol. 7(3), 242-250, 1996.
[26]E. Ordentlich and M. Weinberger and G.Seroussi, “A low-complexity modelling approach for embedded coding of wavelet coefficients”, Proceeding of IEEE Data Compression Conference, 408-417, 1998.
[27]J. S. Walker and T. Q. Nguyen, “Wavelet based image compression”, chap. 6, in: K.R. Rao, P. Yip (Eds), Handbook on transform and data compression CRC press Boca Raton, 267-312, 2001.
[28]Y. Yuan and M. K. Mandal, “Noval embedded image coding algorithms based on wavelet difference reduction”, Vision, Image and Signal Processing, IEE Proceedings, vol. 152(1), 9-19, 2005.
[29]J. S. Walker, “Lossy image codec based on adaptively scanned wavelet difference reduction”, Optical En-gineering, Optical Engineering, vol. 39(7), 1891-1897, 1995.
[30]J. S. Walker and T. Q. Nguyen, “Adaptive scanning methods for wavelet difference reduction in lossy image compression”, Proceeding of IEEE Interna-tional Conference on Image Processing, 3, 182-185, 2000.
[31]A. R. Calderbank, I. Daubechies, W. Sweldens and B. L. Yeo, “Wavelet transforms that map integers to integers”, Appl. Comput. Harmonics Anal, vol. 5, 332-369, 1998.
[32]I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps”, J. Fourier Anal. Appl, vol. 4, 245-267, 1998.
[33]W. Sweldens, “The lifting scheme: a construction of second generation wavelets”, SIAM J. Math. Anal., vol. 29, 511-546, 1998.
[34]Henrique S. Malvar, Gary J. Sullivan and Sridhar Srinivasan, “Lifting-based reversible color transfor-mations for image compression”, Proc. SPIE, 2008.
[35]B. Yuan and X. Tang “(Eds., Image data processing in the compressed wavelet domain”, 3rd International Conf. on Signal Processing Proc., vol. 147, 978-981, 1996.
[36]S. C. Pei and J. J. Ding, “Reversible Integer Color Transform”, IEEE Transactions on Image Processing, vol. 16, 1686–1691, 2007.
[37]I. Singh, P. Agathoklis and A.Antoniou, “A Loss-less compression of color images using an improved integer-based nonlinear wavelet transform”, IEEE international symposium on circuits and systems, 1997.
[38]R. Starosolski, “New simple and efficient color space transformations for lossless image compression”, J. Vis. Commun. Image, vol. 25, 1056–1063, 2014.
[39]S. Kumari “A Wavelet Based Approach for Compression of Color images”, I.J.Modern Education and Computer Science, Vol. 1, 28-35, 2013.
[40]B. Mohammed, Performance Comparison of DWT compared to DCT for Compression of Biomedical Images,”, I.J.Modern Education and Computer Science, Vol. 4, 9-16, 2014.