Skip to main content
SpringerLink
Log in

A prediction-based lossless image compression procedure using dimension reduction and Huffman coding

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Advanced therapeutic imaging innovation produces an immense amount of information, predominantly from processed tomography and other imaging modalities. This causes a significant challenge when storing them on a local personal computer or communicating them over cyberspace. Therefore, a proficient image compression system is fundamentally required. From this perspective, this paper proposes a lossless image compression procedure by reducing image dimension and using a prediction technique. In the proposed strategy, the column dimension of a grey-scale image is first reduced and then the prediction errors are encoded using Huffman coding. The decoding process is carried out in the reverse direction. The proposed method is executed and applied to several bench-marked images. The performance of this proposed algorithm is assessed and compared with the state-of-the-art techniques based on several assessment criteria, such as average code length (ACL), compression ratio (CR), encoding time, decoding time, efficiency, peak signal to noise ratio (PSNR) and normalised correlation (NC). The proposed algorithm also demonstrates an improvement in the average code length compared with the state-of-the-art techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Akhtar MB, Qureshi AM (2011) Qamar-ul-islam “Optimized run length coding for jpeg imagecompression used in space research program of IST”. In: International conference on computer networks and information technology. Abbottabad, pp 81–85. https://doi.org/10.1109/ICCNIT.2011.6020912

  2. Al-Himyari B (2008) A hybrid compression algorithm by using Shannon-Fano coding and oring bits. J Kerbala Univ, 6(3)

  3. Anandan P, Sabeenian R (2016) Medical image compression using wrapping based fast discrete curvelet transform and arithmetic coding. Circuits Syst 07(08):2059–2069

    Article  Google Scholar 

  4. Badshah G, Liew S, Zain J, Ali M (2015) Watermark compression in medical imagewatermarking using lempel-ziv-welch (lzw) lossless compression technique. J Digital Imag 29(2):216–225

    Article  Google Scholar 

  5. Balakrishnan S, Rexy C, Kumar S (2017) 2017 Performance study on facial image compression using master code. In: 2017 world congress on computing and communication technologies (WCCCT). Tiruchirappalli, pp 246–249. https://doi.org/10.1109/WCCCT.2016.67

  6. Barni M (2006) Document and image compression. Crc press

  7. Bell TC, Cleary JG, Witten IH (1990) Text compression, vol 348. Prentice Hall, Englewood Cliffs

    Google Scholar 

  8. Brunello D, Calvagno G, Mian GA, Rinaldo R (2003) Lossless compression of video using temporal information. IEEE Trans image Process 12(2):132–139

    Article  MathSciNet  Google Scholar 

  9. Crow B (2020) Bill crow’s digital imaging & photography Blog.Docs.microsoft.com. https://docs.microsoft.com/en-us/archive/blogs/billcrow/, (Accessed 5 Feb, 2021)

  10. De Simone F, Goldmann L, Baroncini V, Ebrahimi T (2009) Subjective evaluation of JPEG XR image compression. In: Proceedings of the applications of digital image processing XXXII. International society for optics and photonics, San Diego, pp 3–5

  11. Elad M, Goldenberg R, Kimmel R (2007) Low bit-rate compression of facial images. IEEE Trans Image Process 16(9):2379–2383

    Article  MathSciNet  Google Scholar 

  12. Furht B, Akar E, Andrews WA (2018) Lossless JPEG image compression. In: Digital image processing: practical approach. Springer briefs in computer science. Springer, Cham

  13. Ghanbari M (2003) Standard codecs: image compression to advanced video coding (No. 49). Iet.

  14. Ho YS, Kim HJ (2005) 2015. Advances in multimedia information processing-PCM. Springer, Berlin

    Google Scholar 

  15. Huffman DA (1952) A method for the construction of minimum-redundancy codes. Proc IRE 40(9):1098–1101

    Article  MATH  Google Scholar 

  16. Kathirvalavakumar T, Palaniappan R (2011) Modified run-length encoding method and distance algorithm to classify run-length encoded binary data. Commun Comput Inf Sci: 271–280

  17. Kekre H, Sange S, Sawant G, Lahoty A (2011) Image compression using Halftoning and Huffman coding. Commun Comput Inf Sci: 221–226

  18. Lamorahan C, Pinontoan B, Nainggolan N (2013) Data compression using shannon-fano algorithm. de CARTE-SIAN 2(2):10–17

    Article  Google Scholar 

  19. Li J (2003) Image compression: the mathematics of JPEG 2000. Modern Signal Process 46:185– 221

    MathSciNet  MATH  Google Scholar 

  20. Malik A, Sikka G, Verma HK (2017) A high capacity text steganography scheme based on Huffman compression and colour coding. J Inf Optim Sci 38(5):647–664

    MathSciNet  Google Scholar 

  21. Marcellin MW, Gormish MJ, Bilgin A, Boliek MP (2000) An overview of JPEG-2000. In: Proceedings DCC 2000. Data compression conference, pp 523–541. IEEE

  22. Masmoudi A, Mahmoud A (2013) A new arithmetic coding model for a block-based losslessimage compression based on exploiting inter-block correlation. SIViP 9(5):1021–1027

    Article  Google Scholar 

  23. Miaou SG, Ke FS, Chen SC (2009) A lossless compression method for medical image sequences using JPEG-LS and interframe coding. IEEE Trans Inf Technol Biomed 13(5):818–821

    Article  Google Scholar 

  24. Miller FP, Vandome AF, McBrewster J (2009) Audio compression (data): data compression, Streaming media, audio file format, algorithm, computer software, audio codec Lossless data compression, lossy (information theory), coding theory. Alpha Press

  25. Nandi U, Mandal J (2016) Wavelet-Based Image compression using SPIHT and windowed huffman coding with limited distinct symbol and it’s variant. AdvIntell Syst Comput: 435–441

  26. Patel R, Katiyar S, Arora K (2016) An improved image compression technique usinghuffman coding and FFT. In: International conference on smart trends for information technology and computer communications. Springer, Singapore, pp 54–61

  27. Pennebaker WB (1992) Mitchell JL, JPEG, still image data compression standard. Springer Science & Business Media

  28. Praisline Jasmi R, Perumal B, Rajasekaran Pallikonda M (2015) “Comparison of image compression techniques using Huffman coding, DWT and fractal algorithm.” In: 2015 international conference on computer communication and informatics (ICCCI). Coimbatore, pp 1–5. https://doi.org/10.1109/ICCCI.2015.7218137https://doi.org/10.1109/ICCCI.2015.7218137

  29. Rabbani M (2002) JPEG2000: image compression fundamentals, standards and practice. J Electron Imaging 11(2):286

    Article  Google Scholar 

  30. Rahman MA, Hamada M (2019) A semi-lossless image compression procedure using a lossless mode of JPEG. In: 2019 IEEE 13th International symposium on embedded multicore/many-core systems-on-chip (MCSoC). Singapore, pp 143–148. https://doi.org/10.1109/MCSoC.2019.00028

  31. Rahman MA, Fazle Rabbi MM, Rahman MM, Islam MM, Islam MR (2018) Histogram modification based lossy image compression scheme using Huffman coding. In: 2018 4th international conference on electrical engineering and information & communication technology (iCEEiCT). Dhaka, pp 279–284. https://doi.org/10.1109/CEEICT.2018.8628092

  32. Rahman M, Hamada M (2019) Lossless image compression techniques: a state-of-the-art survey. Symmetry 11(10):1274

    Article  Google Scholar 

  33. Rahman MA, Hamada M, Shin J (2021) The Impact of state-of-the-art techniques for lossless still image compression. Electronics 10(3):360. https://doi.org/10.3390/electronics10030360

    Article  Google Scholar 

  34. Rahman MA, Rabbi MF (2015) DWT-SVD based new watermarking idea in RGB colour space. I J Signal Process Image Process Pattern Recognit 8 (6):193–198. https://doi.org/10.14257/ijsip.2015.8.6.20

    Google Scholar 

  35. Rahman MA, Shin J, Saha AK, Islam RM (2018) A novel lossless coding technique for image compression. In: 2018 Joint 7th international conference on informatics, electronics & vision (ICIEV) and 2018 2nd international conference on imaging, vision & pattern recognition (icIVPR). Kitakyushu, pp 82–86. https://doi.org/10.1109/ICIEV.2018.8641065

  36. Reddy CS, Murthy CR (2017) Image compression using complex wavelet transform (CWT) withcustom thresholding. In: International conference of electronics, communication and aerospace technology (ICECA). Coimbatore, pp 308–312. https://doi.org/10.1109/ICECA.2017.8203693

  37. Rissanen J, Langdon GG (1979) Arithmetic Coding. IBM J Res Dev 23(2):149–162. https://doi.org/10.1147/rd.232.0149https://doi.org/10.1147/rd.232.0149

    Article  MathSciNet  MATH  Google Scholar 

  38. Sahoo R, Roy S, Chaudhuri S.S (2014) Haar wavelet transform image compression using run length encoding. In: 2014 international conference on communication and signal processing. Melmaruvathur, pp 071–075, https://doi.org/10.1109/ICCSP.2014.6949801

  39. Said A (2004) Introduction to arithmetic coding-theory and practice. Hewlett Packard Laboratories Report, pp 1057–7149

  40. Sangeetha M, Betty P, Kumar GSN (2017) A biometrie iris image compression using LZW and hybrid LZW coding algorithm. In: 2017 International conference on innovations in information, embedded and communication systems (ICIIECS). Coimbatore, pp 1–6. https://doi.org/10.1109/ICIIECS.2017.8275906https://doi.org/10.1109/ICIIECS.2017.8275906

  41. Saravanan C, Ponalagusamy R (2009) Lossless grey-scale image compression using source symbols reduction and Huffman coding. Int J Image Process 3(5):246

    Google Scholar 

  42. Schaefer G, Starosolski R, Zhu SY (2006) An evaluation of lossless compression algorithms for medical infrared images. In: 2005 IEEE engineering in medicine and biology 27th annual conference. IEEE, pp 1673–1676

  43. Shsu.edu. (2020) Never-Cow image database, raw audio database, steganogrpahy, forgery database, raw image database, raw audio database https://www.shsu.edu/qxl005/New/Downloads/index.html, Accessed 18 Apr 2020

  44. Song MS (2008) Entropy encoding in wavelet image compression. In: Jorgensen PET, Merrill KD, Packer JA (eds) Representations, wavelets, and frames. Applied and numerical harmonic analysis. Birkhäuser, Boston

  45. Swedish Nomad (2020) Countries with the fastest internet in the world 2020 - Swedish Nomad. https://www.swedishnomad.com/fastest-internet-in-the-world, Accessed 29 Mar 2020

  46. Taubman DS, Marcellin MW (2002) Introduction to JPEG 2000. In: JPEG2000 Image compression fundamentals, standards and practice. The Springer international series in engineering and computer science, vol 642. Springer, Boston

  47. Vaidya M, Walia ES, Gupta A (2014) Data compression using Shannon-fano algorithm implemented by VHDL. In: 2014 International conference on advances in engineering & technology research (ICAETR - 2014). Unnao, pp 1–5. https://doi.org/10.1109/ICAETR.2014.7012798

  48. Vaidya M, Walia ES, Gupta A (2014) Data compression using Shannon-Fano algorithm implemented by VHDL. In: 2014 International conference on advances in engineering & technology research (ICAETR-2014). Unnao, pp 1–5. https://doi.org/10.1109/ICAETR.2014.7012798

  49. Wang YD (2005) The implementation of undistorted dynamic compression technique for biomedical image. Master’s thesis, Dept Electr Eng Nat, Cheng Kung University, Taiwan

  50. Weinberger MJ, Seroussi G, Sapiro G (1996) LOCO-I A low complexity, context-based, lossless image compression algorithm. In: Proceedings of data compression conference-DCC’96. Snowbird, pp 140–149

  51. Witten I, Neal R, Cleary J (1987) Arithmetic coding for data compression. Commun ACM 30(6):520–540. https://doi.org/10.1145/214762.214771

    Article  Google Scholar 

  52. Wu X, Memon N (2000) Context-based lossless interband compression-extending CALIC. IEEE Trans Image Process 9(6):994–1001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City, Fukushima, Japan

    Md. Atiqur Rahman & Mohamed Hamada

Authors
  1. Md. Atiqur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Hamada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Atiqur Rahman.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahman, M.A., Hamada, M. A prediction-based lossless image compression procedure using dimension reduction and Huffman coding. Multimed Tools Appl 82, 4081–4105 (2023). https://doi.org/10.1007/s11042-022-13283-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13283-3

Keywords

Search

Navigation