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Improving image encoding quality with a low-complexity DCT approximation using 14 additions

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Abstract

The quality of images is crucial in image and video compression, especially for resource-constrained systems that prioritize simplicity. To achieve fast and low-energy compression, such systems aim to strike a balance between image quality and computational complexity. While various Discrete Cosine Transform (DCT) approximations have been proposed, only two approximations with 14 additions are currently available. This paper presents a novel 8-point DCT approximation that improves image quality compared to the previous 14-addition transformations. Additionally, a pruned version is derived and shown to be efficient. The proposed approximation achieves an average quality gain of up to 1 dB while maintaining a similar computational structure to the previous transformations, resulting in comparable energy consumption. Therefore, this solution provides a compelling option for resource-constrained systems seeking efficient image compression while preserving high image quality.

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References

  1. Aliouat, A., Kouadria, N., Harize, S., Maimour, M.: An efficient low complexity region-of-interest detection for video coding in wireless visual surveillance. IEEE Access (2023)

  2. Aliouat, A., Kouadria, N., Maimour, M., Harize, S., Doghmane, N.: Region-of-interest based video coding strategy for rate/energy-constrained smart surveillance systems using wmsns. Ad Hoc Netw. 140, 103076 (2023)

    Article  Google Scholar 

  3. Araar, C., Ghanemi, S., Benmohammed, M., Atoui, H.: 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)

    Article  Google Scholar 

  4. Bayer, F., Cintra, R.: Dct-like transform for image compression requires 14 additions only. Electron. Lett. 48(15), 1 (2012)

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

  6. Bouguezel, S., Ahmad, M.O., Swamy, M.: Low-complexity 8\(\times\) 8 transform for image compression. Electron. Lett. 44(21), 1249–1250 (2008)

    Article  Google Scholar 

  7. Bouguezel, S., Ahmad, M.O., Swamy, M.: A fast 8\(\times\) 8 transform for image compression. In: 2009 International Conference on Microelectronics-ICM, pp. 74–77. IEEE (2009)

  8. Bouguezel, S., Ahmad, M.O., Swamy, M.: A novel transform for image compression. In: 2010 53rd IEEE International Midwest Symposium on Circuits and Systems, pp. 509–512. IEEE (2010)

  9. Bouguezel, S., Ahmad, M.O., Swamy, M.: A low-complexity parametric transform for image compression. In: 2011 IEEE International Symposium of Circuits and Systems (ISCAS), pp. 2145–2148. IEEE (2011)

  10. Brahimi, N., Bouden, T., Brahimi, T., Boubchir, L.: Lossy image compression based on efficient multiplier-less 8-points dct. Multimed. Syst. 28(1), 171–182 (2022)

    Article  Google Scholar 

  11. Britanak, V., Yip, P.C., Rao, K.R.: Discrete Cosine and Sine Transforms: General Properties. Fast Algorithms and Integer Approximations. Elsevier, Amsterdam (2010)

    Google Scholar 

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

    Article  Google Scholar 

  13. Cintra, R.J., Bayer, F.M., Tablada, C.: Low-complexity 8-point dct approximations based on integer functions. Signal Process. 99, 201–214 (2014)

    Article  Google Scholar 

  14. Clark, R.: Relation between the karhunen-loeve and cosine transform. Proc. IEEE 128(11), 359–360 (1981)

    MathSciNet  Google Scholar 

  15. 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 Proc. 12, 247–255 (2016)

    Article  Google Scholar 

  16. Da Silveira, T.L., Canterle, D.R., Coelho, D.F., Coutinho, V.A., Bayer, F.M., Cintra, R.J.: A class of low-complexity dct-like transforms for image and video coding. IEEE Trans. Circ. Syst. Video Technol. (2021)

  17. Harize, S., Mefoued, A., Kouadria, N., Doghmane, N.: Hevc transforms with reduced elements bit depth. Electron. Lett. 54(22), 1278–1280 (2018)

    Article  Google Scholar 

  18. Haweel, T.I.: A new square wave transform based on the dct. Signal Process. 81(11), 2309–2319 (2001)

    Article  MATH  Google Scholar 

  19. Higham, N.J.: Computing the polar decomposition-with applications. SIAM J. Sci. Stat. Comput. 7(4), 1160–1174 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  20. Huynh-Thu, Q., Ghanbari, M.: Scope of validity of psnr in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)

    Article  Google Scholar 

  21. Jeong, S., Jeong, S., Woo, S.S., Ko, J.H.: An overhead-free region-based jpeg framework for task-driven image compression. Pattern Recogn. Lett. 165, 1–8 (2023)

    Article  Google Scholar 

  22. Jridi, M., Alfalou, A., Meher, P.K.: A generalized algorithm and reconfigurable architecture for efficient and scalable orthogonal approximation of dct. IEEE Trans. Circ. Syst. I Regul. Pap. 62(2), 449–457 (2014)

    Article  Google Scholar 

  23. Kasban, H., Nassar, S., El-Bendary, M.A.: Medical images transmission over wireless multimedia sensor networks with high data rate. Analog Integr. Circ. Sig. Process 108(1), 125–140 (2021)

    Article  Google Scholar 

  24. Khalili Sadaghiani, A., Forouzandeh, B.: Low-power hardware-efficient memory-based dct processor. J. Real-Time Image Proc. 19(6), 1105–1121 (2022)

    Article  Google Scholar 

  25. Kidwai, N.R., Khan, E., Reisslein, M.: Zm-speck: a fast and memoryless image coder for multimedia sensor networks. IEEE Sens. J. 16(8), 2575–2587 (2016)

    Article  Google Scholar 

  26. Kim, S.H., Park, J.H., Ko, J.H.: Target-dependent scalable image compression using a reconfigurable recurrent neural network. IEEE Access 9, 119418–119429 (2021)

    Article  Google Scholar 

  27. 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 

  28. Lee, S.W., Kim, H.Y.: An energy-efficient low-memory image compression system for multimedia iot products. EURASIP J. Image Video Process. 2018, 1–15 (2018)

    Article  Google Scholar 

  29. Mechouek, K., Kouadria, N., Doghmane, N., Kaddeche, N.: Low complexity dct approximation for image compression in wireless image sensor networks. J. Circ. Syst. Comput. 25(08), 1650088 (2016)

    Article  Google Scholar 

  30. Mohanty, B.K.: Approximate lifting 2-d dwt hardware design for image encoder of wireless visual sensors. IEEE Sens. J. (2023)

  31. Monika, R., Dhanalakshmi, S.: An efficient medical image compression technique for telemedicine systems. Biomed. Signal Process. Control 80, 104404 (2023)

    Article  Google Scholar 

  32. Oliveira, L.M., Rodrigues, J.J.: Wireless sensor networks: a survey on environmental monitoring. J. Commun. 6(2), 143–151 (2011)

    Article  Google Scholar 

  33. Oliveira, R.S., Cintra, R.J., Bayer, F.M., da Silveira, T.L., Madanayake, A., Leite, A.: Low-complexity 8-point dct approximation based on angle similarity for image and video coding. Multidimension. Syst. Signal Process. 30, 1363–1394 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  34. 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. Circ. Syst. I Regul. Pap. 61(6), 1727–1740 (2014)

    Article  Google Scholar 

  35. Sakhri, A., Hadji, O., Bouarrouguen, C., Maimour, M., Kouadria, N., Benyahia, A., Rondeau, E., Doghmane, N., Harize, S.: Audio-visual low power system for endangered waterbirds monitoring. IFAC-PapersOnLine 55(5), 25–30 (2022)

    Article  Google Scholar 

  36. Shidik, G.F., Noersasongko, E., Nugraha, A., Andono, P.N., Jumanto, J., Kusuma, E.J.: A systematic review of intelligence video surveillance: trends, techniques, frameworks, and datasets. IEEE Access 7, 170457–170473 (2019)

    Article  Google Scholar 

  37. of Southern California, U.: The usc-sipi image database http://sipi.usc.edu/database/. Signal and Image Processing Institute (2011)

  38. Wallace, G.K.: The jpeg still picture compression standard. IEEE Trans. Consumer Electron. 38(1), xviii–xxxiv (1992)

  39. Wang, Q., Shen, L., Shi, Y.: Recognition-driven compressed image generation using semantic-prior information. IEEE Signal Process. Lett. 27, 1150–1154 (2020)

    Article  Google Scholar 

  40. Wang, Z.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  41. 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(4), 600–612 (2004)

    Article  Google Scholar 

  42. Žádník, J., Mäkitalo, M., Vanne, J., Jääskeläinen, P.: Image and video coding techniques for ultra-low latency. ACM Comput. Surv. (CSUR) 54(11s), 1–35 (2022)

    Article  Google Scholar 

  43. Zidani, N., Kouadria, N., Doghmane, N., Harize, S.: Low complexity pruned dct approximation for image compression in wireless multimedia sensor networks. In: 2019 5th International Conference on Frontiers of Signal Processing (ICFSP), pp. 26–30. IEEE (2019)

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Authors and Affiliations

  1. Laboratory of Automatic and Signals of Annaba (LASA), Electronics Department, Faculty of Technology, Badji Mokhtar-Annaba University, Annaba 23000, Algeria

    Abdelkader Mefoued, Nasreddine Kouadria, Saliha Harize & Noureddine Doghmane

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  1. Abdelkader Mefoued
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  2. Nasreddine Kouadria
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  3. Saliha Harize
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  4. Noureddine Doghmane
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Contributions

AM: Conceptualization; Methodology; Data curation; Formal analysis; Investigation; Software; Roles/Writing – original draft preparation. NK: Supervision; Validation; Software; Writing - review and editing. SH: Validation; Software; Writing - review and editing. ND: Validation; Software; Writing – review and editing.

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Correspondence to Abdelkader Mefoued.

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Mefoued, A., Kouadria, N., Harize, S. et al. Improving image encoding quality with a low-complexity DCT approximation using 14 additions. J Real-Time Image Proc 20, 58 (2023). https://doi.org/10.1007/s11554-023-01315-6

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