Pruned improved eight-point approximate DCT for image encoding in visual sensor networks requiring only ten additions


A low-complexity pruned eight-point discrete cosine transform (DCT) approximation for image compression in visual sensor networks is introduced. The proposed transform consists of using an approximate DCT in combination with pruning approach. The aim of the former is to reduce the computational complexity by not computing the DCT exactly, while the latter aims at computing only the more important low-frequency coefficients. An algorithm for the fast computation of the proposed transform is developed. Only ten additions are required for both forward and backward transformations. The proposed pruned DCT transform exhibits extremely low computational complexity while maintaining competitive image compression performance in comparison with the state-of-the-art methods. An efficient parallel-pipelined hardware architecture for the proposed pruned DCT is also designed. The resulting design is implemented on Xilinx Virtex-6 XC6VSX475T-2ff1156 FPGA technology and evaluated for hardware resource utilization, power consumption, and real-time performance. All the metrics we investigated showed clear advantages of the proposed pruned approximate transform over the state-of-the-art competitors.

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  1. 1.

    Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002).

    Article  Google Scholar 

  2. 2.

    Rahimi, M., Baer, R., Iroezi, O.I., Garcia, J.C., Warrior, J., Estrin, D., Srivastava, M.: Cyclops: in situ image sensing and interpretation in wireless sensor networks. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys’05), USA, pp. 192–204 (2005).

  3. 3.

    Soro, S., Heinzelman, W.: A survey of visual sensor networks. Adv. Multimed. 2009, 1–19 (2009).

    Article  Google Scholar 

  4. 4.

    Akyildiz, I.F., Melodia, T., Chowdhury, K.R.: A survey on wireless multimedia sensor networks. Comput. Netw. 51(4), 921–960 (2007).

    Article  Google Scholar 

  5. 5.

    Mammeri, A., Hadjou, B., Khoumsi, A.: A survey of image compression algorithms for visual sensor networks. ISRN Sens. Netw. 2012, 1–19 (2012).

    Article  Google Scholar 

  6. 6.

    Lee, D., Kim, H., Rahimi, M., Estrin, D.: Energy-efficient image compression for resource constrained platforms. IEEE Trans. Image Process. 18(9), 2100–2113 (2009).

    MathSciNet  Article  MATH  Google Scholar 

  7. 7.

    Mammeri, A., Khoumsi, A., Ziou, D., Hadjou, B.: Energy-aware JPEG for visual sensor networks. In: Maghrebian Conference on Information Technologies (MCSEAI’08), Oran, Algeria (2008)

  8. 8.

    Taylor, C.N., Panigrahi, D., Dey, S.: Design of an adaptive architecture for energy efficient wireless image communication. Lect. Notes Comput. Sci. 2268, 260–273 (2002).

    Article  MATH  Google Scholar 

  9. 9.

    Cintra, R.J., Bayer, F.M.: A DCT approximation for image compression. IEEE Signal Process. Lett. 18(10), 579–582 (2011).

    Article  Google Scholar 

  10. 10.

    Bayer, F.M., Cintra, R.J.: DCT-like transform for image compression requires 14 additions only. Electron. Lett. 48(15), 919–921 (2012).

    Article  Google Scholar 

  11. 11.

    Bouguezel, S., Ahmad, M.O., Swamy, M.N.S.: Binary discrete cosine and hartley transforms. IEEE Trans. Circuits Syst. 60(4), 989–1002 (2013).

    MathSciNet  Article  Google Scholar 

  12. 12.

    Potluri, U.S., Madanayake, A., Cintra, R.J., Bayer, F.M., Kulasekera, S., Edirisuriya, A.: Improved 8-point approximate DCT for image and video compression requiring only 14 additions. IEEE Trans. Circuits Syst. 61(6), 1727–1740 (2014).

    Article  Google Scholar 

  13. 13.

    Tablada, C.J., da Silveira, T.L.T., Cintra, R.J., Bayer, F.M.: DCT approximations based on Chen’s factorization. Signal Process. Image Commun. 58, 14–23 (2017).

    Article  Google Scholar 

  14. 14.

    Oliveira, R.S., Cintra, R.J., Bayer, F.M., da Silveira, T.L.T., Madanayake, A., Leite, A.: Low-complexity 8-point DCT approximation based on angle similarity for image and video coding. Multidimens. Syst. Signal Process. (2018).

    Article  MATH  Google Scholar 

  15. 15.

    Jridi, M., Meher, P.K.: Scalable approximate DCT architectures for efficient HEVC-compliant video coding. IEEE Trans. Circuits Syst. Video 27(8), 1815–1825 (2017).

    Article  Google Scholar 

  16. 16.

    Makkaoui, L., Lecuire, V., Moureaux, J.-M.: Fast zonal DCT-based image compression for wireless camera sensor networks. In: Proceedings of the IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA’10), Paris, France, pp. 126–129 (2010)

  17. 17.

    Lecuire, V., Makkaoui, L., Moureaux, J.-M.: Fast zonal DCT for energy conservation in wireless image sensor networks. Electron. Lett. 48, 125–127 (2012).

    Article  Google Scholar 

  18. 18.

    Kouadria, N., Doghmane, N., Messadeg, D., Harize, S.: Low complexity DCT for image compression in wireless visual sensor networks. Electron. Lett. 49(24), 1531–1532 (2013).

    Article  Google Scholar 

  19. 19.

    Mechouek, K., Kouadria, N., Doghmane, N., Kaddeche, N.: Low complexity DCT approximation for image compression in wireless image sensor networks. J. Circuits Syst. Comput. 25(8), 1650088 (2016).

    Article  Google Scholar 

  20. 20.

    Araar, C., Ghanemi, S., Benmohammed M., Bourennane, E.: Low complexity image compression using pruned 8-point DCT approximation in wireless visual sensor networks. In: 2017 International Conference on Mathematics and Information Technology (ICMIT), Adrar, Algeria, pp. 279–285 (2017).

  21. 21.

    Coutinho, V.A., Cintra, R.J., Bayer, F.M., Kulasekera, S., Madanayake, A.: A multiplierless pruned DCT-like transformation for image and video compression that requires ten additions only. J. Real-Time Image Process. 12(2), 247–255 (2015).

    Article  Google Scholar 

  22. 22.

    Britanak, V., Yip, P., Rao, K.R.: Discrete cosine and sine transforms. Academic Press, San Diego (2007)

    Google Scholar 

  23. 23.

    Bouguezel, S., Ahmad, M.O., Swamy, M.N.S.: Low-complexity 8 × 8 transform for image compression. Electron. Lett. 44, 1249–1250 (2008).

    Article  Google Scholar 

  24. 24.

    The USC-SIPI image database. University of Southern California, Signal and Image Processing Institute. Accessed 2018

  25. 25.

    Rao, K.R., Yip, P.C.: The Transform and Data Compression Handbook. CRC Press LLC, Boca Raton (2001)

    Google Scholar 

  26. 26.

    Blahut, R.E.: Fast Algorithms for Signal Processing. Cambridge University Press, Cambridge (2010)

    Google Scholar 

  27. 27.

    Loeffler, C., Ligtenberg, A., Moschytz, G.S.: Practical fast 1-D DCT algorithms with 11 multiplications. In: International Conference on Acoustics, Speech, and Signal Processing, Glasgow, UK, pp. 988–991 (1989).

  28. 28.

    Hosny, K.M.: Fast computation of accurate zernike moments. J. Real-Time Image Process. 3(1), 97–107 (2008).

    MathSciNet  Article  Google Scholar 

  29. 29.

    ISO/IEC 10918-1/ITU-T Recommendation T.81, Digital compression and coding of continuous-tone still images. Accessed 2018

  30. 30.

    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).

    Article  Google Scholar 

  31. 31.

    Phamila, A.V.Y., Amutha, R.: Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks. Electron. Lett. 51(11), 824–826 (2015).

    Article  Google Scholar 

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Araar, C., Ghanemi, S., Benmohammed, M. et al. Pruned improved eight-point approximate DCT for image encoding in visual sensor networks requiring only ten additions. J Real-Time Image Proc 17, 1597–1608 (2020).

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  • VSNs
  • Image compression
  • Pruning approach
  • FPGA
  • Low power consumption