Skip to main content
SpringerLink
Log in

An Improved Block Truncation Coding Using Rectangular Non-symmetry and Anti-packing Model

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13393))

Included in the following conference series:

  • 1488 Accesses

Abstract

Block truncated coding (BTC) is an image compression algorithm with a simple coding process and a high coding speed, which can be used in the field of military communications with high real-time requirements. As the price of pursuing simplicity and high speed, the compression ratio and the quality of the decoded image are sacrificed to some extent. Although some strategies have been proposed to improve the compression ratio and the quality of the decoded images, the effect is not obvious. Inspired by the quadtree-based block truncation coding (QEDBTC) and the non-symmetry and anti-packing model (NAM), in this paper, we propose a novel rectangular NAM-based block truncation algorithm (RNAMEDBTC), which uses rectangular NAM strategy to divide the initial blocks into rectangular homogeneous blocks. The spatial frequency measurement (SFM) is used as a measurement parameter to subdivide the initial blocks. For each homogeneous block, we replace the high and low quantization values and the binary bitmaps in the traditional block truncation coding with the average value of the pixels in the block, thereby a great improvement of the compression rate of the algorithm is achieved. In order to further improve the compression rate, we have increased the area of the smaller homogeneous blocks, and thus reducing the number of homogeneous blocks. These expanded blocks are called non-homogeneous blocks. For each non-homogeneous block, we need to do error diffusion block truncation coding (EDBTC) processing. The experimental results in this paper show that without degrading the quality of the decoded image, the proposed algorithm improves the compression rate significantly by 158.3% and 30.8% higher than the traditional BTC and QEDBTC algorithms, respectively.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Delp, E., Mitchell, O.: Image compression using block truncation coding. IEEE Trans. Commun. 27(9), 1335–1342 (1979)

    Article  Google Scholar 

  2. Jiang, M., Yang, H.: Secure outsourcing algorithm of BTC feature extraction in cloud computing. IEEE Access 8, 106958–106967 (2020)

    Article  Google Scholar 

  3. Liu, X., Lin, C.C., Muhammad, K., et al.: Joint data hiding and compression scheme based on modified BTC and image inpainting. IEEE Access 7, 116027–116037 (2019)

    Google Scholar 

  4. Shie, S., Jiang, J., Su, Y., Chang, W.: An improved steganographic scheme implemented on the compression domain of image using BTC and histogram modification. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 640–644. Krakow (2018)

    Google Scholar 

  5. Cheng, H., Chen, C., Lee, L., Lin, T., Chiou, Y., Chen, S.: A low-complexity color image compression algorithm based on AMBTC. In: 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), pp. 1–2 (2019)

    Google Scholar 

  6. Guo, J., Sankarasrinivasan, S.: H-BTC database: a brief review on halftone based block truncation coding (H-BTC) images. In: 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1–2 (2019)

    Google Scholar 

  7. Ke, S., Jhou, H., Chen, C., Lin, T., Abu, P., Chen, S.: A hardware-oriented image compression algorithm based on BTC and YEF color space. In: 2021 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–5 (2021)

    Google Scholar 

  8. Lamsrichan, P.: Straightforward Color image compression using true-mean multi-level block truncation coding. In: 2021 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6 (2021)

    Google Scholar 

  9. Liao, J., Horng, J., Lee, C., Lu, H.: Hiding secret image in absolute moment block truncation code by using a block-selection scheme. In: 2019 8th International Conference on Innovation, Communication and Engineering (ICICE), pp. 39–42 (2019)

    Google Scholar 

  10. Lema, M., Mitchell, O.: Absolute moment block truncation coding and its application to color images. IEEE Trans. Commun. 32(10), 0–1157 (1984)

    Google Scholar 

  11. Guo, J.: Improved block truncation coding using modified error diffusion. Electron. Lett. 44(7), 462–464 (2008)

    Google Scholar 

  12. Devi, S., Mathew, A.: Fast image retrieval using error diffusion block truncation coding and unsupervised clustering. In: 2016 International Conference on Emerging Technological Trends (ICETT), pp.1–6 (2016)

    Google Scholar 

  13. Guo, J., Wu, M.: Improved block truncation coding based on the void-and-cluster dithering approach. IEEE Trans. Image Process. 18(1), 211–213 (2008)

    MathSciNet  MATH  Google Scholar 

  14. Guo, J., Liu, Y.: Improved block truncation coding using optimized dot diffusion. IEEE Trans Image Process 23(3), 1269–1275 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Liu, Y., Guo, J., Wu, Z., et al.: Near-aperiodic dot-diffused block truncation coding. Signal Process. 120, 373–384 (2015)

    Article  Google Scholar 

  16. Guo, J., Sankarasrinivasan, S.: Enhanced block truncation coding image using digital multitone screen. In: 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 672–676. Kuala Lumpur (2017)

    Google Scholar 

  17. Guo, J.M., Sankarasrinivasan, S.: Reconstruction of multitone BTC images using conditional generative adversarial nets. In: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 814–817. Lanzhou, China (2019)

    Google Scholar 

  18. Guo, J., Sankarasrinivasan, S.: H-BTC database: a brief review on halftone based block truncation coding (H-BTC) images. In: 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1–2. Taipei, Taiwan (2019)

    Google Scholar 

  19. Yang, F., Lien, C., Chen, P., et al.: An efficient quadtree-based block truncation coding for digital image compression. In: Thirtieth International Conference on Advanced Information NETWORKING and Applications Workshops, pp. 939–942. IEEE (2016)

    Google Scholar 

  20. Zheng, Y., Chen, C.: Study on a new algorithm for gray image representation. Chin. J. Comput. 33(12), 2397–2406 (2010)

    Article  Google Scholar 

  21. Zheng, Y., Yang, B., Sarem, M.: Hierarchical image segmentation based on nonsymmetry and anti-packing pattern representation model. IEEE Trans. Image Process. 30, 2408–2421 (2021)

    Article  Google Scholar 

  22. Zheng, Y., Chen, C.: A color image representation method based on non-symmetry and anti-packing model. J. Software 18(11), 2932–2941 (2007)

    Article  Google Scholar 

Download references

Acknowledgement

This work is supported by the Natural Science Foundation of Guangdong Province of China under Grant No. 2017A030313349 and No. 2021A1515011517, the National Natural Science Foundation of China under Grant No. 61300134, and the National Undergraduate Innovative and Entrepreneurial Training Program under Grant No. 202110561070 and No.202110561066.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Yunping Zheng, Yuan Xu & Jinjun Kuang

  2. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China

    Mudar Sarem

  3. General Organization of Remote Sensing, Damascus, Syria

    Mudar Sarem

Authors
  1. Yunping Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinjun Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mudar Sarem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunping Zheng .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi'an Polytechnic University, Xi'an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, Y., Xu, Y., Kuang, J., Sarem, M. (2022). An Improved Block Truncation Coding Using Rectangular Non-symmetry and Anti-packing Model. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-13870-6_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13869-0

  • Online ISBN: 978-3-031-13870-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation