Advertisement

DC Coefficients Recovery from AC Coefficients in the JPEG Compression Scenario

  • Han Qiu
  • Qinkai Zheng
  • Meikang QiuEmail author
  • Gerard Memmi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)

Abstract

With the deployment of multimedia compression techniques, contents such as images or videos are transmitted through resource-constrained networks such as Internet of Things (IoT) scenarios. Traditional multimedia compression methods based on spatial-frequency transforms and coding techniques have been progressively improving and approaching the theoretical limit of Shannon. Therefore some new approaches are proposing to transmit only a part of the compressed data and recover the missing part at the receiver’s end. In this paper, we propose to follow this idea to highly enhance the JPEG compression by transmitting all AC coefficients and only four DC coefficients of one image. On the receiver’s end, we propose two methods to rebuild the missing DC coefficients based on the remaining DCT coefficient relationships. The first method considers the pixel relationship compared with the adjacent blocks on multiple directions. The second method considers not only the pixel relationship between adjacent blocks but also the existing relationship inside the adjacent blocks. As a result, our proposed method recovers the transmitted JPEG image with more than 25 dB considering PSNR while transmitting only 40–60% of the size of the JPEG images.

Keywords

DCT Image compression JPEG Image transmission Dc coefficients recovery 

References

  1. 1.
    Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90–93 (1974)MathSciNetCrossRefGoogle Scholar
  2. 2.
    David, R., John, G., John, R.: Data age 2025: the digitization of the world, from edge to core. In: IDC White Paper. Seagate (2018)Google Scholar
  3. 3.
    Fisher, Y.: Fractal Image Compression: Theory and Application. Springer, Berlin (2012).  https://doi.org/10.1007/978-1-4612-2472-3CrossRefGoogle Scholar
  4. 4.
    Kresch, R., Merhav, N.: Fast DCT domain filtering using the DCT and the DST. IEEE Trans. Image Process. 8(6), 821–833 (1999)CrossRefGoogle Scholar
  5. 5.
    Li, S., Karrenbauer, A., Saupe, D., Kuo, C.C.J.: Recovering missing coefficients in DCT-transformed images. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 1537–1540. IEEE (2011)Google Scholar
  6. 6.
    Pennebaker, W.B., Mitchell, J.L.: JPEG: Still Image Data Compression Standard. Springer, Berlin (1992)Google Scholar
  7. 7.
    Qiu, H., Enfrin, N., Memmi, G.: A case study for practical issues of DCT based bitmap selective encryption methods. In: IEEE International Conference on Security of Smart Cities, Industrial Control System and Communications. IEEE (2018)Google Scholar
  8. 8.
    Qiu, H., Memmi, G., Chen, X., Xiong, J.: DC coefficient recovery for JPEG images in ubiquitous communication systems. Future Gener. Comput. Syst. 96, 23–31 (2019)CrossRefGoogle Scholar
  9. 9.
    Qiu, H., Noura, H., Qiu, M., Ming, Z., Memmi, G.: A user-centric data protection method for cloud storage based on invertible DWT. IEEE Trans. Cloud Comput. (2019) Google Scholar
  10. 10.
    Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Trans. Circuits Syst. Video Technol. 17(9), 1103–1120 (2007)CrossRefGoogle Scholar
  11. 11.
    Thuraisingham, B.: Data Mining: Technologies, Techniques, Tools, and Trends. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  12. 12.
    Uehara, T., Safavi-Naini, R., Ogunbona, P.: Recovering DC coefficients in block-based DCT. IEEE Trans. Image Process. 15(11), 3592–3596 (2006)CrossRefGoogle Scholar
  13. 13.
    Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), 18–34 (1992)CrossRefGoogle Scholar
  14. 14.
    Zeng, J., Au, O.C., Dai, W., Kong, Y., Jia, L., Zhu, W.: A tutorial on image/video coding standards. In: 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–7. IEEE (2013)Google Scholar
  15. 15.
    Zhang, J., Cox, I.J., Doerr, G.: Steganalysis for LSB matching in images with high-frequency noise. In: 2007 IEEE 9th Workshop on Multimedia Signal Processing. pp. 385–388. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Han Qiu
    • 1
  • Qinkai Zheng
    • 1
  • Meikang Qiu
    • 2
    Email author
  • Gerard Memmi
    • 1
  1. 1.LTCI, Telecom ParisParisFrance
  2. 2.Department of Computer ScienceTexas A&M University-CommerceCommerceUSA

Personalised recommendations