Vol 3 No 1 (2020): Special issue on cyber-security of critical infrastructure

Improving Image Transmission by Using Polar Codes and Successive Cancellation List Decoding

Álvaro Garcia
University of Pernambuco
Maria De Lourdes Melo Guedes Alcoforado
Francisco Madeiro
University of Pernambuco
Valdemar Cardoso da Rocha Jr.
Federal University of Pernambuco
Published November 17, 2020
  • Additive White Gaussian Noise,
  • AWGN,
  • Image Transmission Polar Codes
How to Cite
Garcia, Álvaro, Alcoforado, M. D. L. M. G., Madeiro, F., & da Rocha Jr., V. C. (2020). Improving Image Transmission by Using Polar Codes and Successive Cancellation List Decoding. Annals of Disaster Risk Sciences, 3(1). https://doi.org/10.51381/adrs.v3i1.41


This paper investigates the transmission of grey scale images encoded with polar codes and de-coded with successive cancellation list (SCL) decoders in the presence of additive white Gaussian noise. Po-lar codes seem a natural choice for this application be-cause of their error-correction efficiency combined with fast decoding. Computer simulations are carried out for evaluating the influence of different code block lengths in the quality of the decoded images. At the encoder a default polar code construction is used in combination with binary phase shift keying modulation. The results are compared with those obtained by using the clas-sic successive cancellation (SC) decoding introduced by Arikan. The quality of the reconstructed images is assessed by using peak signal to noise ratio (PSNR) and the structural similarity (SSIM) index. Curves of PSNR and SSIM versus code block length are presented il-lustrating the improvement in performance of SCL in comparison with SC.


  1. Abot, J., Olivier, C., Perrine, C., & Pousset, Y. (2012, November). A link adaptation scheme optimized for wireless JPEG 2000 transmission over real- istic MIMO systems. Signal Processing: Im- age Communication, 27(10), 1066–1078. doi: 10.1016/j.image.2012.08.003
  2. Alencar, M. S. (2009). Digital television systems. New York, NY, USA: Cambridge University Press.
  3. Arikan, E. (2009, July). Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memory- less channels. IEEE Transactions on Information Theory, 55(7), 3051-3073. doi: 10.1109/TIT.2009.2021379
  4. Azevedo, R. A., Madeiro, F., Lopes, W. T. A., & Lima, E. A. O. (2016, March). A quasi ran- dom symbol interleaving technique applied to image transmission by noisy channels. IEEE Latin America Transactions, 14(3), 1078-1085. doi: 10.1109/TLA.2016.7459582
  5. Carlton, A. (2017, March). 5G and future mobile. Networkworld, http://www.networkworld.com/article/3151866/mobile- wireless/ surprise-polar-codes-are-coming-in-from-the-cold.htm.
  6. Dhandapani, V., & Ramachandran, S. (2014). Area and power efficient DCT architecture for image com- pression. EURASIP Journal on Advances in Sig- nal Processing(1), 2014:180. doi: 10.1186/1687- 6180-2014-180
  7. Drury, G., Markarian, G., & Pickavance, K. (2001). Coding and modulation for digital television. London, UK: Kluwer Academic Publishers.
  8. Gallager, R. G. (2001). Low density parity check codes. Cambridge, Massachusetts, USA: MIT Press.
  9. Hanhart, P., Bernardo, M. V., Pereira, M., G. Pinheiro, A. M., & Ebrahimi, T. (2015). Benchmark- ing of objective quality metrics for HDR im- age quality assessment. EURASIP Journal on Image and Video Processing(1), 2015:39. doi: 10.1186/s13640-015-0091-4
  10. Hashemi, S. A., Condo, C., & Gross, W. J. (2016). A fast polar code list decoder architecture based on sphere decoding. IEEE Transactions on Circuits and Systems I: Regular Papers, 63(12), 2368– 2380.
  11. Hashemi, S. A., Condo, C., & Gross, W. J. (2017). Fast and flexible successive-cancellation list decoders for polar codes. IEEE Transactions on Signal Processing, 65(21), 5756–5769.
  12. Jin, L., Li, Y., Zhao, C., Wei, Z., Li, B., & Shi, J.
  13. (2016). Cascading polar coding and lt coding for radar and sonar networks. EURASIP Journal on Wireless Communications and Networking(1), 2016:254. doi: 10.1186/s13638-016-0748-4
  14. Liu, L., Wang, A., Chang, C.-C., & Li, Z. (2014, Jan- uary). A novel real-time and progressive se- cret image sharing with flexible shadows based on compressive sensing. Signal Processing: Image Communication, 29(1), 128–134. doi: 10.1016/j.image.2013.10.003
  15. Mishra, A., Sharma, K., & De, A. (2014, January). Quality image transmission through awgn chan- nel using polar codes. International Journal of Computer Science and Telecommunication, 5.
  16. Nikolakopoulos, G., Kandris, D., & Tzes, A. (2010). Adaptive compression of slowly varying images transmitted over wireless sensor networks. Sensors, 10(8), 7170. doi: 10.3390/s100807170
  17. Niu, K., Chen, K., Lin, J., & Zhang, Q. T. (2014, July). Polar codes: Primary concepts and practical decoding algorithms. IEEE Com- munications Magazine, 52(7), 192-203. doi: 10.1109/MCOM.2014.6852102
  18. Payommai, T., & Chamnongthai, K. (2013, Nov). Performance of polar code for image trans- mission. In 2013 international symposium on intelligent signal processing and communication systems (p. 450-453). doi: 10.1109/IS- PACS.2013.6704592
  19. Qazi, S. A., Shoaib, M., Javaid, U., & Asif, S. (2009). A comparative analysis of LDPC decoders for image transmission over AWGN channel. In Proceedings of the 7th international confer- ence on frontiers of information technology (pp. 4:1–4:5). New York, NY, USA: ACM. doi: 10.1145/1838002.1838007
  20. Sarisaray-Boluk, P., Gungor, V. C., Baydere, S., & Har- manci, A. E. (2011, September). Quality aware image transmission over underwater multimedia sensor networks. Ad Hoc Networks., 9(7), 1287– 1301. doi: 10.1016/j.adhoc.2011.02.007
  21. Sarkis, G., Giard, P., Vardy, A., Thibeault, C., & Gross, W. J. (2014, May). Fast polar decoders: Al- gorithm and implementation. IEEE Journal on Selected Areas in Communications, 32(5), 946- 957. doi: 10.1109/JSAC.2014.140514
  22. Sarkis, G., Giard, P., Vardy, A., Thibeault, C., & Gross, W. J. (2016a). Fast list decoders for polar codes. IEEE Journal on Selected Areas in Communica- tions, 34(2), 318–328.
  23. Sarkis, G., Giard, P., Vardy, A., Thibeault, C., & Gross, W. J. (2016b, Feb). Fast list decoders for polar codes. IEEE Journal on Selected Areas in Communications, 34(2), 318-328. doi: 10.1109/JSAC.2015.2504299
  24. Tal, I., & Vardy, A. (2015, May). List decod- ing of polar codes. IEEE Transactions on Information Theory, 61(5), 2213-2226. doi: 10.1109/TIT.2015.2410251
  25. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004, April). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600- 612. doi: 10.1109/TIP.2003.819861
  26. Wei, Z., Li, B., & Zhao, C. (2015). On the po- lar code for the 60-GHz millimeter-wave systems. EURASIP Journal on Wireless Communications and Networking. doi: 10.1186/s13638- 015-0264-y
  27. Wen, J., Ma, C., & Zhao, J. (2014). FIVQ algorithm for interference hyper-spectral image compression. Optics Communications, 322, 97 - 104. doi: http://dx.doi.org/10.1016/j.optcom.2014.02.016
  28. Zhao, S., Shi, P., & Wang, B. (2011, Nov). Polar codes and its application in speech communication. In 2011 international conference on wireless com- munications and signal processing (wcsp) (p. 1- 4). doi: 10.1109/WCSP.2011.6096731