Advertisement

Prediction-Based Lossless Image Compression

  • Mohamed Uvaze Ahamed AyoobkhanEmail author
  • Eswaran Chikkannan
  • Kannan Ramakrishnan
  • Saravana Balaji Balasubramanian
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms.

Keywords

Fourier descriptor Wavelet-based countourlet transform Fuzzy c-means classifier Prediction errors Entropy encoding 

Notes

Ethical Approval

For this retrospective type of study formal consent is not required.

References

  1. 1.
    Celik M, Tekalp AM, Sharma G (2003) Level-embedded lossless image compression. Acoustics, speech, and signal processing, 2003. In: 2003 IEEE international conference on Proceedings (ICASSP’03), vol 3, pp III–245, IEEEGoogle Scholar
  2. 2.
    Ahamed A, Eswaran C, Kannan R (2018) Lossy image compression based on vector quantization using artificial bee colony and genetic algorithms. Adv Sci Lett 24(2):1134–1137CrossRefGoogle Scholar
  3. 3.
    Ayoobkhan MUA, Chikkannan E, Ramakrishnan K (2017) Lossy image compression based on prediction error and vector quantisation. EURASIP J Image Video Proc 2017 1:35Google Scholar
  4. 4.
    Bovik AC (2009) The essential guide to image processing. Academic PressGoogle Scholar
  5. 5.
    Nasir DM, Sayood K (1995) Lossless image compression: a comparative study. In: IS&T/SPIE’s symposium on electronic imaging: science and technology. In: International society for optics and photonics, pp 8–20Google Scholar
  6. 6.
    Ramakrishnan K, Eswaran C (2007) Lossless compression schemes for ECG signals using neural network predictors. EURASIP J Appl Sig Proc 2007, 1:102–102Google Scholar
  7. 7.
    Hung CH, Hang HM (2012) A reduced-complexity image coding scheme using decision directed wavelet-based contourlet transform. J Vis Commun Image Represent 23(7):1128–1143CrossRefGoogle Scholar
  8. 8.
    Venkateswaran K, Kasthuri N, Alaguraja R (2015) Performance comparison of wavelet and contourlet frame based features for improving classification accuracy in remote sensing images. J Indian Soc Remote Sens 43(4):729–737CrossRefGoogle Scholar
  9. 9.
    Zhang D, Lu G (2003) A comparative study of curvature scale space and fourier descriptors for shape-based image retrieval. J Vis Commun Image Represent 14(1):39–57CrossRefGoogle Scholar
  10. 10.
    Sokic E, Konjicija S (2016) Phase preserving fourier descriptor for shape-based image retrieval. Sig Process Image Commun 40:82–96CrossRefGoogle Scholar
  11. 11.
    Hung CC, Kulkarni S, Kuo BC (2011) A new weighted fuzzy c-means clustering algorithm for remotely sensed image classification. IEEE J Sel Top Sign Proces 5(3):543–553CrossRefGoogle Scholar
  12. 12.
    Pandey D, Kumar R (2012) Hybrid algorithm using fuzzy c-means and local binary patterns for image indexing and retrieval. In: Soft computing techniques in vision science. Springer, pp 115–125Google Scholar
  13. 13.
    Fabija´nska A (2009) A fuzzy segmentation method for images of heat-emitting objects. In: Iberoamerican congress on pattern recognition. Springer, pp 217–224Google Scholar
  14. 14.
    Oliveira FD, Haas HL, Gomes JGR, Petraglia A (2013) Cmos imager with focal-plane analog image compression combining dpcm and vq. IEEE Trans Circuits Syst I Regul Pap 60(5):1331–1344CrossRefGoogle Scholar
  15. 15.
    Mielikainen J, Huang B (2012) Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length. IEEE Geosci Remote Sens Lett 9(6):1118–1121CrossRefGoogle Scholar
  16. 16.
    Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modelling approach. IEEE Trans Pattern Anal Mach Intell 25(9):1075–1088CrossRefGoogle Scholar
  17. 17.
    Tommasi T, Caputo B, Welter P, Guld MO, Deserno TM (2009) Overview of the CLEF 2009 medical image annotation track. In: Workshop of the cross-language evaluation forum for european languages. Springer, pp 85–93Google Scholar
  18. 18.
    Mohamed Ahamed A, Uvaze C, Eswaran R Kannan (2017) CBIR system based on prediction errors. J Inf Sci Eng 33(2):347–365MathSciNetGoogle Scholar
  19. 19.
    Li H, Chai Y, Li Z (2013) Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection. Optik-Int J Light Electron Opt 124(1):40–51CrossRefGoogle Scholar
  20. 20.
    Ayoobkhan MUA, Chikkannan E, Ramakrishnan K (2018) Feed-forward neural network-based predictive image coding for medical image compression. Arabian J Sci Eng 43(8):4239–4247CrossRefGoogle Scholar
  21. 21.
    Abo-Zahhad M, Gharieb RR, Ahmed SM, Abd-Ellah MK (2015) Huffman image compression incorporating dpcm and dwt. J Sig Inf Proc 6(02):123CrossRefGoogle Scholar
  22. 22.
    Wu X, Memon N (1997) Context-based, adaptive, lossless image coding. IEEE Trans Commun 45(4):437–444CrossRefGoogle Scholar
  23. 23.
    Zhou J, Liu X, Au OC, Tang YY (2014) Designing an efficient image encryption-then compression system via prediction error clustering and random permutation. IEEE Trans Inf Forensics Secur 9(1):39–50CrossRefGoogle Scholar
  24. 24.
    Liu W, Zeng W, Dong L, Yao Q (2010) Efficient compression of encrypted grayscale images. IEEE Trans Image Process 19(4):1097–1102MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohamed Uvaze Ahamed Ayoobkhan
    • 1
    Email author
  • Eswaran Chikkannan
    • 2
  • Kannan Ramakrishnan
    • 2
  • Saravana Balaji Balasubramanian
    • 1
    • 3
  1. 1.Computer Science DepartmentCihan University-ErbilErbilIraq
  2. 2.Centre for Visual Computing, Faculty of Computing and InformaticsMultimedia UniversityCyberjayaMalaysia
  3. 3.Department of Information TechnologyLebanese French UniversityErbilIraq

Personalised recommendations