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Approximate Computing in Image Compression and Denoising

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Approximate Computing

Abstract

Approximate computing techniques have been widely used to design fault-tolerant image processing applications. This chapter presents two novel energy-efficient approximate techniques; an image compression technique, and an image denoising technique. The image compression algorithm modifies the conventional DCT compression scheme to achieve around 70% reduction in required adders (and therefore energy) while maintaining the quality of compressed images. On the other hand, the image denoising technique introduces an additional step length parameter to the conventional unconstrained total variation-based inexact Newton scheme. The proposed scheme retains quality and requires fewer iterations making it a faster and more energy-efficient alternative.

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References

  1. Almurib HA, Kumar TN, Lombardi F. Inexact designs for approximate low power addition by cell replacement. In: 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany. IEEE; 2016. p. 660–5.

    Google Scholar 

  2. Jiang H, Han J, Lombardi F. A comparative review and evaluation of approximate adders. In: Proceedings of the 25th edition on Great Lakes Symposium on VLSI. ACM; 2015. p. 343–8.

    Chapter  Google Scholar 

  3. Junqi H, Kumar TN, Abbas H, Lombardi F. A deterministic low-complexity approximate (multiplier-less) technique for DCT computation. IEEE Trans Circuits Syst I Regul Pap. 2019;66(8):3001–14.

    Article  MathSciNet  Google Scholar 

  4. Gupta V, Mohapatra D, Raghunathan A, Roy K. Low-power digital signal processing using approximate adders. IEEE Trans Comput Aided Des Integr Circuits Syst. 2013;32(1):124–37.

    Article  Google Scholar 

  5. Hegde R, Shanbhag NR. Soft digital signal processing. IEEE Trans Very Large Scale Integr VLSI Syst. 2001;9(6):813–23.

    Article  Google Scholar 

  6. Chakrapani LN, Muntimadugu KK, Lingamneni A, George J, Palem KV. Highly energy and performance efficient embedded computing through approximately correct arithmetic: a mathematical foundation and preliminary experimental validation. In: 2008 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, Atlanta, GA, USA. ACM; 2008. p. 187–96.

    Google Scholar 

  7. Verma AK, Brisk P, Ienne P. Variable latency speculative addition: a new paradigm for arithmetic circuit design. In: Proceedings of the Conference on Design, Automation and Test in Europe. IEEE; 2008. p. 1250–5.

    Chapter  Google Scholar 

  8. Kulkarni P, Gupta P, Ercegovac MD. Trading accuracy for power in a multiplier architecture. J Low Power Electron. 2011;7(4):490–501.

    Article  Google Scholar 

  9. Lingamneni A, Enz C, Nagel J-L, Palem K, Piguet C. Energy parsimonious circuit design through probabilistic pruning. In: 2011 Design, Automation & Test in Europe, Grenoble, France. IEEE; 2011. p. 1–6.

    Google Scholar 

  10. Kahng AB, Kang S. Accuracy-configurable adder for approximate arithmetic designs. In: Proceedings of the 49th Annual Design Automation Conference, San Francisco, California. IEEE; 2012. p. 820–5.

    Chapter  Google Scholar 

  11. Ye R, Wang T, Yuan F, Kumar R, Xu Q. On reconfiguration-oriented approximate adder design and its application. In: 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Jose, CA, USA. IEEE; 2013. p. 48–54.

    Google Scholar 

  12. Bouguezel S, Ahmad MO, Swamy M. A low-complexity parametric transform for image compression. In: 2011 IEEE International Symposium of Circuits and Systems (ISCAS), Rio de Janeiro, Brazil. IEEE; 2011. p. 2145–8.

    Chapter  Google Scholar 

  13. Sadhvi Potluri U, Madanayake A, Cintra RJ, Bayer FM, Kulasekera S, Edirisuriya A. Improved 8-point approximate DCT for image and video compression requiring only 14 additions. IEEE Trans Circuits Syst I Regul Pap. 2014;61(6):1727–40.

    Article  Google Scholar 

  14. Cintra RJ, Bayer FM, Coutinho VA, Kulasekera S, Madanayake A, Leite A. Energy-efficient 8-point DCT approximations: theory and hardware architectures. Circuits Syst Signal Process. 2016;35(11):4009–29.

    Article  Google Scholar 

  15. Bayer FM, Cintra RJ. DCT-like transform for image compression requires 14 additions only. Electron Lett. 2012;48(15):919–21.

    Article  Google Scholar 

  16. Jdidia SB, Jridi M, Belghith F, Masmoudi N. Low-complexity algorithm using DCT approximation for POST-HEVC standard. In: Pattern Recognition and Tracking XXIX, Orlando, Florida, United States, vol. 10649. SPIE; 2018. p. 106490Y.

    Google Scholar 

  17. Nocedal J, Wright S. Numerical optimization. New York: Springer Science & Business Media; 2006. p. 664.

    MATH  Google Scholar 

  18. Peyré G. The numerical tours of signal processing – advanced computational signal and image processing. IEEE Comput Sci Eng. 2011;13(4):94–7.

    Article  Google Scholar 

  19. Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D. 1992;60(1–4):259–68.

    Article  MathSciNet  Google Scholar 

  20. Hegde R, Shanbhag NR. Energy-efficient signal processing via algorithmic noise-tolerance. In: Proceedings of the 1999 International Symposium on Low Power Electronics and Design, San Diego, CA, USA. IEEE; 1999. p. 30–5.

    Google Scholar 

  21. Hegde R, Shanbhag NR. A voltage overscaled low-power digital filter IC. IEEE J Solid-State Circuits. 2004;39(2):388–91.

    Article  Google Scholar 

  22. Mohapatra D, Karakonstantis G, Roy K. Significance driven computation: a voltage-scalable, variation-aware, quality-tuning motion estimator. In: Proceedings of the 2009 ACM/IEEE International Symposium on Low Power Electronics and Design, San Francisco, CA, USA. ACM; 2009. p. 195–200.

    Chapter  Google Scholar 

  23. Junqi H, Abbas H, Kumar TN, Fabrizio L. An inexact Newton method for unconstrained total variation-based image denoising by approximate addition. In: IEEE Transactions on Emerging Topics in Computing (Early Access). IEEE; 2021. p. 1.

    Google Scholar 

  24. Peyré G. The numerical tours of signal processing. Comput Sci Eng. 2011;13(4):94–7.

    Article  Google Scholar 

  25. Vogel C, Oman M. Iterative methods for total variation denoising. SIAM J Sci Comput. 1996;17(1):227–38.

    Article  MathSciNet  Google Scholar 

  26. Chambolle A, Levine SE, Lucier BJ. Some variations on total variation-based image smoothing. Institute for Mathematics and Its Applications, University of Minnesota; 2009.

    Google Scholar 

  27. C. V. Group. CVG-UGR image database. 2019. Available: http://decsai.ugr.es/cvg/index2.php.

  28. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. New Delhi: McGraw Hill Education; 2010. p. 738.

    Google Scholar 

  29. Gonzalez RC, Woods RE. Digital image processing. 4th ed. New York: Pearson; 2018. p. 1019.

    Google Scholar 

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Correspondence to Haider Abbas F. Almrib .

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Huang, J., Almrib, H.A.F., Nandha Kumar, T., Lombardi, F. (2022). Approximate Computing in Image Compression and Denoising. In: Liu, W., Lombardi, F. (eds) Approximate Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-98347-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-98347-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98346-8

  • Online ISBN: 978-3-030-98347-5

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