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COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS

Year 2016, Volume: 29 Issue: 4, 971 - 980, 20.12.2016

Abstract

Run Length Encoding (RLE) is one of the simplest and primitive lossless data compression technique. RLE sometimes doubles the size of compressed data stream. To overcome this disadvantage, several algorithms have been introduced, one of which being Mespotine RLE (MRLE). This paper introduces modification to MRLE technique in which the constant size ‘Comp-Bit List’ have been replaced by ‘Variable Size Comp-Bit List’ and refers to the new technique as improved – MRLE (iMRLE) technique. This paper discusses the details of ‘Variable Size Comp-Bit List’ and utilizes this concept for lossless compression and decompression of 8-bit grayscale medical images and extends the concept to 16-bit grayscale medical images. Image quality metrics such as Compression Ratio (CR), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Entropy are used to check the quality of decompressed image obtained using iMRLE technique. Finally, the compression ratio achieved for existing MRLE and iMRLE techniques for 8-bit and 16-bit grayscale images have been assessed and iMRLE is found to produce best results for lossless compression and decompression of medical images

References

  • Khalid Sayood, “Introduction to Data Compression”, Newnes Publications, 2012, ISBN 978-012-41-5796-5
  • Mohammad Ali Kakhaee, Farokh Marvasti, “A Survey on Digital Data Hiding Schemes: Principals, Algorithms, and Applications”, The ISC International Journal of Information Security 5.1: 5, 2013
  • Omar Adil Mahdi, Mazin Abed Mohammed, Ahmed Jasim Mohamed, “Implementing a novel approach an convert audio compression to text coding via hybrid technique”, International Journal of Computer Science Issues 9.6: 53-59, 2012
  • Kishore A Kotteri, Amy E. Bell, Joan E. Carletta, “Design of multiplierless, high-performance, wavelet filter banks with image compression applications”, Circuits and Systems I: Regular Papers, IEEE Transactions on 51.3: 483-494, 2004
  • Qin Lu, “Low-complexity and energy efficient image compression scheme for wireless sensor networks”, Computer Networks 52.13: 2594-2603, 2008
  • Amine Nait-Ali, Christine Cavaro-Menard, “Compression of Biomedical Images and Signals”, John Wiley & Sons, 2008, ISBN: 978-1-84821-028-8
  • Asha Latha, Permender Singh, “Review of Image Compression Techniques”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001: 2008 Certified Journal, Volume 3, Issue 7, 2013
  • Konstantinos N, Plataniotis, Anastasios N. Venetsanopoulos, “Color image processing and applications”, Springer-Verlag New York, 2013, ISBN: 3-540-66953-1 560
  • Uvais Qidwai, C.H. Chen, “Digital Image Processing: An Algorithmic Approach with MATLAB”, Chapman and Hall/CRC, 2009, ISBN 9781420079500
  • Roger Bourne, “Fundamentals of Digital Imaging in Medicine”, Springer-Verlag London, 2010, ISBN 978-1-84882-086-9.
  • P. Mansfield, I. L. Pykett, “Biological and medical imaging by NMR”, Journal of Magnetic Resonance, 213.2: 513-531, 2011
  • Qiang Yang, Hua Jun Wang, Xue Gang Luo, “An Improved Algorithm for Color Medical Image Compression Based on DCT”, Applied Mechanics and Materials, Vol. 602, 2014
  • Ruchika, Mooninder Singh, Anant Raj Singh, “Compression of Medical Images Using Wavelet Transforms”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, 2012
  • D. Smutek, “Quality measurement of lossy compression in medical imaging”, Prague medical report 106.1: 5-26, 2005
  • Michael Lustig, “Compressed sensing MRI”, Signal Processing Magazine, IEEE 25.2: 72-82, 2008
  • Shaou-Gang Miaou, Fu-Sheng Ke, Shu-Ching Chen, “A lossless compression method for medical image sequences using JPEG-LS and interframe coding”, Information Technology in Biomedicine, IEEE Transactions on 13.5: 818-821, 2009
  • S. Bhavani, K. Thanushkodi, “A survey on coding algorithms in medical image compression”, International Journal on Computer Science and Engineering 2.05: 1429-1434, 2010
  • Ming-Bo Lin, Jang-Feng Lee, Gene Eu Jan (2006); “A Lossless Data Compression and Decompression Algorithm and its Hardware Architecture”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 14, No. 9: 925 – 936
  • Salomon, David, Giovanni Motta, “Handbook of data compression”, Springer Science & Business Media, 2010
  • B.W.R Agung, F. P. Permana, “Medical image watermarking with tamper detection and recovery using reversible watermarking with LSB modification and run length encoding compression, Communication”, Networks and Satellite (ComNetSat), IEEE International Conference, 2012
  • Meo Mespotine, “Mespotine-RLE-basic v0. 9-An overhead-reduced and improved Run-Length-Encoding Method”, arXiv preprint arXiv: 1501.05542, 2015
  • Yu-Chen Hu, Chin-hen Chang, “A new lossless compression scheme based on Huffman coding scheme for image compression”, Signal Processing: Image Communication 16.4: 367-372, 2010
  • K. Rajeswari, K. Kavitha, G. Boopathi Raja, “High Efficient Image Compression Using Lempel-Ziv-Welch Algorithm”, Digital Image Processing 7.2: 44-47, 2015
  • Jiaji Wu, “Arithmetic coding for image compression with adaptive weight-context classification”, Signal Processing: Image Communication 28.7: 727-735, 2013
  • Fred Halsall, “Multimedia Communications – Applications, Networks, Protocols and Standards”, Pearson Education Ltd, 2008, ISBN – 978-81-317-0994-8
  • Tokuhiro Tsukiyama, “Method and system for data compression and restoration”, U.S. Patent No. 4,586,027, 1986
  • Edward L Hauck, “Data compression using run length encoding and statistical encoding”, U.S. Patent No. 4,626,829, 1986
  • Douglas C Stevens, “Bit-wise run-length encoding for data compression”, U.S. Patent No. 5,049,880, 1991
  • G. Davis, “Parallel run length encoding compression: Reducing I/O in dynamic environmental simulations”, International Journal of High Performance Computing Applications 12.4: 396-410, 1998
  • Ling-fang Zhu, Ren-ren Liu, “DCT and RLE Mixed Lossy Compression of Gray Image Based on Matlab”, Computer Knowledge and Technology 21: 074, 2009
  • P. Hemnath, V. Prabhu, “Compression of FPGA bitstreams using improved RLE algorithm”, Information Communication and Embedded Systems (ICICES) International Conference on. IEEE, 2013
  • Hasan Demirel, Gholamreza Anbarjafari, “Discrete wavelet transform-based satellite image resolution enhancement”, Geoscience and Remote Sensing, IEEE Transactions on 49.6: 483-495, 2011
  • J.C Yoo,; C. W. Ahn, “Image matching using peak signal-to-noise ratio- based occlusion detection”, Image Processing, IET 6.5: 483-495, 2012
  • M. Rangaraj Rangayyan, “Biomedical image analysis”, CRC press, 2004
Year 2016, Volume: 29 Issue: 4, 971 - 980, 20.12.2016

Abstract

References

  • Khalid Sayood, “Introduction to Data Compression”, Newnes Publications, 2012, ISBN 978-012-41-5796-5
  • Mohammad Ali Kakhaee, Farokh Marvasti, “A Survey on Digital Data Hiding Schemes: Principals, Algorithms, and Applications”, The ISC International Journal of Information Security 5.1: 5, 2013
  • Omar Adil Mahdi, Mazin Abed Mohammed, Ahmed Jasim Mohamed, “Implementing a novel approach an convert audio compression to text coding via hybrid technique”, International Journal of Computer Science Issues 9.6: 53-59, 2012
  • Kishore A Kotteri, Amy E. Bell, Joan E. Carletta, “Design of multiplierless, high-performance, wavelet filter banks with image compression applications”, Circuits and Systems I: Regular Papers, IEEE Transactions on 51.3: 483-494, 2004
  • Qin Lu, “Low-complexity and energy efficient image compression scheme for wireless sensor networks”, Computer Networks 52.13: 2594-2603, 2008
  • Amine Nait-Ali, Christine Cavaro-Menard, “Compression of Biomedical Images and Signals”, John Wiley & Sons, 2008, ISBN: 978-1-84821-028-8
  • Asha Latha, Permender Singh, “Review of Image Compression Techniques”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001: 2008 Certified Journal, Volume 3, Issue 7, 2013
  • Konstantinos N, Plataniotis, Anastasios N. Venetsanopoulos, “Color image processing and applications”, Springer-Verlag New York, 2013, ISBN: 3-540-66953-1 560
  • Uvais Qidwai, C.H. Chen, “Digital Image Processing: An Algorithmic Approach with MATLAB”, Chapman and Hall/CRC, 2009, ISBN 9781420079500
  • Roger Bourne, “Fundamentals of Digital Imaging in Medicine”, Springer-Verlag London, 2010, ISBN 978-1-84882-086-9.
  • P. Mansfield, I. L. Pykett, “Biological and medical imaging by NMR”, Journal of Magnetic Resonance, 213.2: 513-531, 2011
  • Qiang Yang, Hua Jun Wang, Xue Gang Luo, “An Improved Algorithm for Color Medical Image Compression Based on DCT”, Applied Mechanics and Materials, Vol. 602, 2014
  • Ruchika, Mooninder Singh, Anant Raj Singh, “Compression of Medical Images Using Wavelet Transforms”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, 2012
  • D. Smutek, “Quality measurement of lossy compression in medical imaging”, Prague medical report 106.1: 5-26, 2005
  • Michael Lustig, “Compressed sensing MRI”, Signal Processing Magazine, IEEE 25.2: 72-82, 2008
  • Shaou-Gang Miaou, Fu-Sheng Ke, Shu-Ching Chen, “A lossless compression method for medical image sequences using JPEG-LS and interframe coding”, Information Technology in Biomedicine, IEEE Transactions on 13.5: 818-821, 2009
  • S. Bhavani, K. Thanushkodi, “A survey on coding algorithms in medical image compression”, International Journal on Computer Science and Engineering 2.05: 1429-1434, 2010
  • Ming-Bo Lin, Jang-Feng Lee, Gene Eu Jan (2006); “A Lossless Data Compression and Decompression Algorithm and its Hardware Architecture”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 14, No. 9: 925 – 936
  • Salomon, David, Giovanni Motta, “Handbook of data compression”, Springer Science & Business Media, 2010
  • B.W.R Agung, F. P. Permana, “Medical image watermarking with tamper detection and recovery using reversible watermarking with LSB modification and run length encoding compression, Communication”, Networks and Satellite (ComNetSat), IEEE International Conference, 2012
  • Meo Mespotine, “Mespotine-RLE-basic v0. 9-An overhead-reduced and improved Run-Length-Encoding Method”, arXiv preprint arXiv: 1501.05542, 2015
  • Yu-Chen Hu, Chin-hen Chang, “A new lossless compression scheme based on Huffman coding scheme for image compression”, Signal Processing: Image Communication 16.4: 367-372, 2010
  • K. Rajeswari, K. Kavitha, G. Boopathi Raja, “High Efficient Image Compression Using Lempel-Ziv-Welch Algorithm”, Digital Image Processing 7.2: 44-47, 2015
  • Jiaji Wu, “Arithmetic coding for image compression with adaptive weight-context classification”, Signal Processing: Image Communication 28.7: 727-735, 2013
  • Fred Halsall, “Multimedia Communications – Applications, Networks, Protocols and Standards”, Pearson Education Ltd, 2008, ISBN – 978-81-317-0994-8
  • Tokuhiro Tsukiyama, “Method and system for data compression and restoration”, U.S. Patent No. 4,586,027, 1986
  • Edward L Hauck, “Data compression using run length encoding and statistical encoding”, U.S. Patent No. 4,626,829, 1986
  • Douglas C Stevens, “Bit-wise run-length encoding for data compression”, U.S. Patent No. 5,049,880, 1991
  • G. Davis, “Parallel run length encoding compression: Reducing I/O in dynamic environmental simulations”, International Journal of High Performance Computing Applications 12.4: 396-410, 1998
  • Ling-fang Zhu, Ren-ren Liu, “DCT and RLE Mixed Lossy Compression of Gray Image Based on Matlab”, Computer Knowledge and Technology 21: 074, 2009
  • P. Hemnath, V. Prabhu, “Compression of FPGA bitstreams using improved RLE algorithm”, Information Communication and Embedded Systems (ICICES) International Conference on. IEEE, 2013
  • Hasan Demirel, Gholamreza Anbarjafari, “Discrete wavelet transform-based satellite image resolution enhancement”, Geoscience and Remote Sensing, IEEE Transactions on 49.6: 483-495, 2011
  • J.C Yoo,; C. W. Ahn, “Image matching using peak signal-to-noise ratio- based occlusion detection”, Image Processing, IET 6.5: 483-495, 2012
  • M. Rangaraj Rangayyan, “Biomedical image analysis”, CRC press, 2004
There are 34 citations in total.

Details

Journal Section Electrical & Electronics Engineering
Authors

Shiva Putra This is me

H.s. Sheshadri This is me

V. Lokesha This is me

Publication Date December 20, 2016
Published in Issue Year 2016 Volume: 29 Issue: 4

Cite

APA Putra, S., Sheshadri, H., & Lokesha, V. (2016). COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS. Gazi University Journal of Science, 29(4), 971-980.
AMA Putra S, Sheshadri H, Lokesha V. COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS. Gazi University Journal of Science. December 2016;29(4):971-980.
Chicago Putra, Shiva, H.s. Sheshadri, and V. Lokesha. “COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS”. Gazi University Journal of Science 29, no. 4 (December 2016): 971-80.
EndNote Putra S, Sheshadri H, Lokesha V (December 1, 2016) COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS. Gazi University Journal of Science 29 4 971–980.
IEEE S. Putra, H. Sheshadri, and V. Lokesha, “COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS”, Gazi University Journal of Science, vol. 29, no. 4, pp. 971–980, 2016.
ISNAD Putra, Shiva et al. “COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS”. Gazi University Journal of Science 29/4 (December 2016), 971-980.
JAMA Putra S, Sheshadri H, Lokesha V. COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS. Gazi University Journal of Science. 2016;29:971–980.
MLA Putra, Shiva et al. “COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS”. Gazi University Journal of Science, vol. 29, no. 4, 2016, pp. 971-80.
Vancouver Putra S, Sheshadri H, Lokesha V. COMP-BIT-LIST SIZE IMPROVEMENT IN MESPOTINE RLE AND ITS APPLICATIONS. Gazi University Journal of Science. 2016;29(4):971-80.