Skip to main content
Log in

Adaptive and dynamic multi-grouping scheme for absolute moment block truncation coding

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

Abstract

Image compression technique is widely used in multimedia signal processing. As a conventional lossy compression technique, block truncation coding (BTC) deserves further improvements to enhance its performance of compression. The improvements of BTC mainly focus on: 1) enhancing the quality of reconstructed image and 2) decreasing the bit rate. In this paper, an adaptive and dynamic multi-grouping scheme is proposed for the absolute moment block truncation coding (AMBTC), which is mainly based on an optimized grouping mechanism with the adaptive threshold setting according to the complexity of image blocks. Besides, the values of the reconstruction levels are replaced by their compressed difference values in order to decrease the bit rate. Experimental results demonstrate that the proposed scheme can enhance the compression performance of AMBTC effectively.

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

Access this article

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

Similar content being viewed by others

References

  1. Al-Azawi S, Boussakta S, Yakovlev A (2011) Low complexity image compression algorithm using AMBTC and bit plane squeezing. Proceedings of international workshop on systems, signal processing and their applications, WOSSPA, Tipaza, pp 131–134

  2. Al-Salhi YEA, Lu S (2017) New steganography scheme to conceal a large amount of secret messages using an improved-AMBTC algorithm based on hybrid adaptive neural networks. Proceedings of IEEE international conference on intelligent data and security, Beijing, pp 112–121

  3. Amarunnishad TM, Govindan VK, Abraham TM (2006) A fuzzy complement edge operator. Proceedings of the fourteenth ieee international conference on advanced computing and communications, Mangalore, Karnataka, India, pp 344–348

  4. Anil D, Karthik KV, Kumar KS (2011) A modified three level block truncation coding for image compression. Proceedings of 2011 international conference on pattern analysis and intelligence robotics, Putrajaya, pp 31–35

  5. Chang CC, Yu YH, Hu YC (2008) Hiding secret data into an AMBTC-compressed image using genetic algorithm. Proceedings of 2008 second international conference on future generation communication and networking symposia, Sanya, pp 154–157

  6. Chang CC, Wu HL, Chung TF (2014) Applying histogram modification to embed secret message in AMBTC. Proceedings of 2014 tenth international conference on intelligent information hiding and multimedia signal processing, Kitakyushu, pp 489–492

  7. Chen LG, Liu YC (1994) A high quality MC-OBTC codec for video signal processing. IEEE Trans Circuits Syst Video Technol 4:92–98

    Article  Google Scholar 

  8. Cheng SC, Tsai WH (1994) Image compression by moment-preserving edge detection. Pattern Recogn 27(11):1439–1449

    Article  Google Scholar 

  9. Delp EJ, Mitchell OR (1979) Image coding using block truncation coding. IEEE Trans Commun 27:1335–1342

    Article  Google Scholar 

  10. Dhara BC, Chanda B (2004) Block truncation coding using pattern fitting. Pattern Recogn 37:2131–2139

    Article  Google Scholar 

  11. Franti P, Nevalainen P, Kaudoranta T (1994) Compression of digital images by block truncation coding: a survey. Comput J 37(4):308–332

    Article  Google Scholar 

  12. Hu YC (2003) Low-complexity and low-bit-rate image compression scheme based on AMBTC. Opt Eng 42:1964–1975

    Article  Google Scholar 

  13. Hu YC (2003) Improved moment preserving block truncation coding for image compression. Electron Lett 39:1377–1379

    Article  Google Scholar 

  14. Hu YC (2004) Predictive moment preserving block truncation coding for gray-level image compression. J Electron Imaging 13:871–877

    Article  Google Scholar 

  15. Lema MD, Mitchell OR (1984) Absolute moment block truncation coding and its application to color image. IEEE Trans Commun 32:1148–1157

    Article  Google Scholar 

  16. Lin CC, Huang Y, Tai WL (2014) Novel image authentication scheme for AMBTC-compressed images. Proceedings of 2014 tenth international conference on intelligent information hiding and multimedia signal processing, Kitakyushu, pp 134–137

  17. Liu JF, Tian YG, Han T, Wang JC, Luo XY (2016) Stego key searching for LSB steganography on JPEG decompressed image. SCIENCE CHINA Inf Sci 59(3):1–15

    Article  Google Scholar 

  18. Ma YY, Luo XY, Li XL, Bao ZK, Zhang Y (2018) Selection of rich model steganalysis features based on decision rough set α-positive region reduction. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2018.2799243

  19. Mathews J, Nair MS, Jo L (2013) Modified BTC algorithm for gray scale images using Max-Min quantizer. Proceedings of 2013 international multi-conference on automation, computing, communication, control and compressed sensing (iMac4s), Kottayam, pp 377–382

  20. Olsen SI (2000) Block truncation and planar image coding. Pattern Recogn Lett 21:1141–1148

    Article  Google Scholar 

  21. Qin C, Hu YC (2016) Reversible data hiding in VQ index table with lossless coding and adaptive switching mechanism. Signal Process 129:48–55

    Article  Google Scholar 

  22. Qin C, Chang CC, Chiu YP (2014) A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Trans Image Process 23(3):969–978

    Article  MathSciNet  Google Scholar 

  23. Qin C, Chen XQ, Ye DP, Wang JW, Sun XM (2016) A novel image hashing scheme with perceptual robustness using block truncation coding. Inf Sci 361–362:84–99

    Article  Google Scholar 

  24. Qin C, Ji P, Wang JW, Chang CC (2017) Fragile image watermarking scheme based on VQ index sharing and self-embedding. Multimed Tools Appl 76(2):2267–2287

    Article  Google Scholar 

  25. Qin C, Ji P, Zhang XP, Dong J, Wang JW (2017) Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process 138:280–293

    Article  Google Scholar 

  26. Qin C, Chen XQ, Luo XY, Zhang XP, Sun XM (2018) Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inf Sci 423:284–302

    Article  MathSciNet  Google Scholar 

  27. Qin C, Ji P, Chang CC, Dong J, Sun XM (2018) Non-uniform watermark sharing based on optimal iterative BTC for image tampering recovery. IEEE Multimedia. https://doi.org/10.1109/MMUL.2018.112142509

    Article  Google Scholar 

  28. Tsou CC, Hu YC, Chang CC (2008) Efficient optimal pixel grouping schemes for AMBTC. Imaging Sci J 56(4):217–231

    Article  Google Scholar 

  29. Vijayanagar KR, Kim J (2012) Compression of residual layers of layered depth video using hierarchical block truncation coding. Proceedings of 2012 3DTV-conference: the true vision - capture, transmission and display of 3D video (3DTV-CON), Zurich, pp 1–4

  30. Yang CY, Lin JC (1996) EBTC: an economical method for searching the threshold of BTC compression. Electron Lett 32:1870–1871

    Article  Google Scholar 

  31. Zhang Y, Qin C, Zhang WM, Liu FL, Luo XY (2018) On the fault-tolerant performance for a class of robust image steganography. Signal Process 146:99–111

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61672354, 61702332), the Open Project Program of the National Laboratory of Pattern Recognition (201600003), the Open Project Program of Shenzhen Key Laboratory of Media Security, Shanghai Engineering Center Project of Massive Internet of Things Technology for Smart Home (GCZX14014), and Hujiang Foundation of China (C14001, C14002).

The authors would like to thank the anonymous reviewers for their valuable suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chuan Qin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, Z., Hu, YC., Yao, H. et al. Adaptive and dynamic multi-grouping scheme for absolute moment block truncation coding. Multimed Tools Appl 78, 7895–7909 (2019). https://doi.org/10.1007/s11042-018-6030-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6030-5

Keywords

Navigation