Skip to main content
SpringerLink
Log in

Low-power hardware-efficient memory-based DCT processor

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

This paper proposes a new discrete cosine transform (DCT) processor. The micro-rotation section of the architecture is based on a shared-resource improved coordinate rotation digital computer (CORDIC) unit, in an enhanced scalable DCT engine. To reduce the resources, and utilization area all micro-rotation operations have implemented as one united block in overlapped form. Using one processing element, the memory-based architecture has reduced the power consumption. Inputs and outputs of the processor are in-order which can be taken into account as an advantage for the proposed design. The processor has a low-complexity and distributed controller. Furthermore, due to the shared-resource implementation of CORDIC-II unit, by reduction of adding, shifting operations both in size, and number, the processor has high capabilities in short word lengths in comparison with state-of-the-art DCT processors. Compared to existing prominent DCT processors, the proposed processor achieves better results with limited hardware resources.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Ahmed, N., Natarajan, T., Rio, K.R.: Discrete cosine transform. IEEE Trans. Comp. 23, 90–93 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  2. Rao, K.R., Yip, P.: Discrete Cosine Transform: Algorithms, Advantages, Applications. Academic Press, San Diego (1990)

    Book  MATH  Google Scholar 

  3. Mitsui, M., Murakami, Y., Obi, T.: Color enhancement in multispectral image using the Karhunen–Loeve transform. Opt. Rev. 12, 69–75 (2005). https://doi.org/10.1007/s10043-004-0069-4

    Article  Google Scholar 

  4. Radünz, A.P., Bayer, F.M., Cintra, R.J.: Low-complexity rounded KLT approximation for image compression. J. Real-Time Image Proc. 19, 173–183 (2022). https://doi.org/10.1007/s11554-021-01173-0

    Article  Google Scholar 

  5. Clarke: Relation between the Karhunen–Loeveand cosine transforms. In: IEE Proceedings F Communications, Radar and Signal Process. 259–260 (1981)

  6. Sadaghiani, A.K., Forouzandeh, B.: Image interpolation based on 2D-DWT and HDP-HMM. Pattern Anal. Appl. 25(2), 361–377 (2022). https://doi.org/10.1007/s10044-022-01057-4

    Article  Google Scholar 

  7. Sadaghiani, A.K., Sheikhaei, S., Forouzandeh, B.: Image interpolation based on 2D-DWT with novel regularity-preserving algorithm using RLS adaptive filters. Int. J. Image Graph. (2022). https://doi.org/10.1142/S0219467823500390

    Article  Google Scholar 

  8. Jiaming, Lu., Zhao, L., Chen, K., Deng, P., Li, B., Liu, S., An, Qi.: Real-time FPGA-based digital signal processing and correction for a small animal PET. IEEE Trans. Nucl. Sci. 66(7), 1287–1295 (2019)

    Article  Google Scholar 

  9. Kumar, S., Jha, R.K.: An FPGA-based design for a real-time image denoising using approximated fractional integrator. Multidimens. Syst. Signal Process. 31, 1317–1339 (2020)

    Article  MATH  Google Scholar 

  10. Britanak, V., Yip, P., Rao, K.R.: Discrete Cosine and Sine Transformes. Academic Press, New York (2007)

    Google Scholar 

  11. International Telecommunication Union recommendation, H.262, Telecommunication Section (2000)

  12. International Telecommunication Union recommendation, H.263, Telecommunication Section (2005)

  13. Bhaskaran, V., Konstantinides, K.: Image and Video Compression Standards. Kluwer, Norwell (1997)

    Book  Google Scholar 

  14. Pourazad, M. T., Doutre, C., Azimi, M., Nasiopoulos, P.: HEVC: The new gold standard for video compression: How does HEVC compare with H.264/AVC?. IEEE Consum. Electron. Mag. 1(3), 36–46 (2012)

  15. Ochoa-Dominguez, H., Rao, K.: Discrete Cosine Transform. CRC Press, Boca Raton (2019)

    Google Scholar 

  16. Huang, H., Xiao, L.: CORDIC based fast Radix-2 DCT algorithm. IEEE Signal Process. Lett. 20(5), 483–486 (2013)

    Article  Google Scholar 

  17. Jridi, M., Alfalou, A.: Joint optimization of low-power DCT architecture and efficient quantization technique for embedded image compression. In: International Federation for Information Processing (IFIP), Brest, France (2012)

  18. Brahimi, N., Bouden, T., Brahimi, T., Boubchir, L.: A novel and efficient 8-point DCT approximation for image compression. Multimed. Tools Appl. 79(1), 7615–7631 (2020)

    Article  Google Scholar 

  19. Oliveira, R.S., Cintra, R.J., Bayer4, F.M., da Silveira, T.L.T.: Low-complexity 8-point DCT approximation based on angle similarity for image and video coding. Multidimens. Syst. Signal Process. 1(21), 1363–1394 (2018)

  20. Shabani, A., Sabri, M., Khabbazan, B., Timarchi, S.: Area and power-efficient variable-sized DCT architecture for HEVC using Muxed-MCM problem. IEEE Trans. Circuits Syst. I: Regul. Pap. 68(3), 1259–1268 (2020)

  21. Singhadia, A., Mamillapalli, M., Chakrabarti, I.: Hardware-efficient 2D-DCT/IDCT architecture for portable HEVC-compliant devices. IEEE Trans. Consum. Electron. 66(3), 203–212 (2020)

    Article  Google Scholar 

  22. Zhang, J., Shi, W., Zhou, Li., Gong, R., Wang, L., Zhou, H.: A low-power and high-PSNR unified DCT/IDCT architecture based on EARC and enhanced scale factor approximation. IEEE Access 7, 165684–165691 (2019)

    Article  Google Scholar 

  23. Shabani, A., Timarchi, S., Mahdavi, H.: Power and area efficient CORDIC-Based DCT using direct realization of decomposed matrix. Microelectron. J. 91, 11–21 (2019)

    Article  Google Scholar 

  24. Chiper, D.F: A structured fast algorithm for the VLSI pipeline implementation of inverse discrete cosine transform. Circuits Syst Signal Process 40, 5351–5366 (2021). https://doi.org/10.1007/s00034-021-01718-5

  25. Parhi, K.K.: VLSI Digital Signal Processing Systems: Design and Implementation. Wiley, New York (1999)

  26. Coelh, D.F.G., Cintra, R.J., Madanayake, A., Perera, S.M.: Low-complexity scaling methods for DCT-II approximations. IEEE Trans. Signal Process. 69, 4557–4566 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  27. Shabani, A., Timarchi, S.: Low-power DCT-based compressor for wireless capsule endoscopy. Signal Process. Image Commun. (2017). https://doi.org/10.1016/j.image.2017.03.003

    Article  Google Scholar 

  28. Garrido, M., Källström, P., Kumm, M., Gustafsson, O.: CORDIC II: a new improved CORDIC algorithm. IEEE Trans. Circuits Syst. II Express Briefs 63(2), 186–190 (2016)

    Article  Google Scholar 

  29. 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. I 61(6), 1727–1740 (2014)

    Article  Google Scholar 

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

  31. Jridi, M., Alfalou, A., Meher, P.K.: A generalized algorithm and reconfigurable architecture for efficient and scalable orthogonal approximation of DCT. IEEE Trans. Circuits Syst.—I: Regul. Pap. 62(2) 449–457 (2015)

  32. Hsiao, J.-H., Chen, L.-G., Chiueh, T.-D., Chen, C.-T.: High throughput CORDIC-based systolic array design for the discrete cosine transform. IEEE Trans. Circuits Syst. Video Technol. 5(3), 218–225 (1995)

    Article  Google Scholar 

  33. Sadaghiani, A.K., Sheikhaei, S.: Hardware-efficient bartlett spectral density estimator based on optimized R22FFT processor using CCSSI method. J. Circuits Syst. Comput. 30(2), 1–20 (2021)

    Article  Google Scholar 

  34. Liu, Bo., Ding, X., Cai, H., Zhu, W., Wang, Z., Liu, W., Yang, J.: Precision adaptive MFCC based on R2SDF-FFT and approximate computing for low-power speech keywords recognition. IEEE Circuits Syst. Mag. 21(4), 24–39 (2021)

    Article  Google Scholar 

  35. Sadaghiani, A.K., Ghanbari, M.: An optimized hardware design for high speed 2DDCT processor based on modified Loeffler architecture. In: 27th Iranian Conference Electrical Engineering (ICEE), Yazd, Iran, pp. 1476–1480 (2019)

  36. Saponara, S.: Real-time and low-power processing of 3D direct/inverse discrete cosine transform for low-complexity video codec. J. Real-Time Image Proc. 7, 43–53 (2012)

    Article  Google Scholar 

  37. Sun, C.-C., Ruan, S.-J., Heyne, B., Goetze, J.: Low-power and high-quality Cordic-based Loeffler DCT for signal processing. IET Circuits Devices Syst. 1(6), 453–461 (2007)

    Article  Google Scholar 

  38. Garrido, M., Qureshi, F., Gustafsson, O.: Low-complexity multiplierless constant rotators based on combined coefficient selection and shift-and-add implementation (CCSSI). IEEE Trans. Circuits Syst. I Regular Pap. 61, 2002–2012 (2014)

    Article  Google Scholar 

  39. Sadaghiani, A.K., Sheikhaei, S., Forouzandeh, B.: Low complexity multiplierless welch estimator based on memory-based FFT. J. Circuits Syst. Comput. (2022). https://doi.org/10.1142/S0218126622200031

    Article  Google Scholar 

  40. Petrovsky, N., Stankevich, A., Petrovsky, A.: CORDIC-lifting factorization of paraunitary filter banks based on the quaternionic multipliers for lossless image coding. Multidimension. Syst. Signal Process. 27, 667–695 (2016)

    Article  MATH  Google Scholar 

  41. https://www.xilinx.com/support/documentation/data_sheets/ds181_Artix_7_Data_Sheet.pdf

  42. Wirendre, A.: Perera: architectures for multiplierless fast Fourier transform hardware implementation in VLSI. IEEE Trans. Acoust. Speech Signal Process. 35(12), 1750–1760 (1987)

    Article  Google Scholar 

  43. Sadaghiani, A.K., Sheikhaei, S., Forouzandeh, B.: High performance image compression based on optimized EZW using hidden Markov chain and Gaussian mixture model. In: 28th Iranian Conference On Electrical Engineering (ICEE), Tabriz, Iran, pp. 1–5 (2020)

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

    Article  Google Scholar 

  45. Ji, X., Kwong, S., Zhao, D., Wang, H., Kuo, C.-C.J., Dai, Q.: Early determination of zero-quantized 8 × 8 DCT coefficients. IEEE Trans. Circuits Syst. Video Technol. 19(12), 1755–1765 (2009)

    Article  Google Scholar 

  46. Cheng, C., Parhi, K.K.: A novel systolic array structure for DCT. IEEE Trans. Circuits Syst. II Express Briefs 52(7), 366–369 (2005)

    Article  Google Scholar 

  47. Wahid, K., Dimitrov, V., Jullien, G.: New encoding of 8 × 8 DCT to make H.264 lossless. In: Proc. IEEE Asia Pacific Conf. Circuits Syst., pp. 780–783 (2006)

  48. Ayas, S., Ekinci, M.: Single image super resolution based on sparse representation using discrete wavelet transform. Multimed. Tools Appl. 77(11), 1–14 (2018). https://doi.org/10.1007/s11042-017-5233-5

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of ECE, College of Engineering, University of Tehran, Amirabad, Tehran, Iran

    AbdolVahab Khalili Sadaghiani & Behjat Forouzandeh

Authors
  1. AbdolVahab Khalili Sadaghiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behjat Forouzandeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to AbdolVahab Khalili Sadaghiani or Behjat Forouzandeh.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khalili Sadaghiani, A., Forouzandeh, B. Low-power hardware-efficient memory-based DCT processor. J Real-Time Image Proc 19, 1105–1121 (2022). https://doi.org/10.1007/s11554-022-01243-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-022-01243-x

Keywords

Search

Navigation