Skip to main content
Log in

FJND-based fuzzy rate control of scalable video for streaming applications

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A fuzzy rate controller with buffer constraint in combination with a perceptual quality controller is proposed for streaming applications of the AVC/H.264 scalable (SVC) video. The bit rate of each video layer is controlled separately by the fuzzy controller that adjusts the quantization parameter (QP) on a group of pictures (GOP) basis. The QPs of pictures are computed from the GOP QP by the well-known QP cascading technique. While the fuzzy controller provides the buffering constraint for each video layer, the quality controller tries to improve the perceptual quality of the compressed video based on the foveated just-noticeable distortion (FJND) model. The quality controller regulates the QP of each macroblock around the picture QP based on the visibility threshold of the FJND model. In these applications, the initial buffering allows slight variations of the bit rate leading to produce a variable bit rate (VBR) video bit stream with consistent quality. Experimental results show that the proposed algorithm effectively adapts to the buffer size, while strictly prevents buffer overflow and underflow. In addition, incorporating the perceptual quality controller into the fuzzy rate controller achieves higher perceptual quality at the same bit rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bakar G, Kirmizioglu RA, Tekalp AM (Feb. 2019) Motion-based rate adaptation in WebRTC videoconferencing using scalable video coding. IEEE Transactions on Multimedia 21(2):429–441

    Article  Google Scholar 

  2. Bjontegaard, G., “Calculation of average PSNR differences between RD-curves,” document VCEG-M33, VCEG contribution, April 2001.

  3. Boyce JM et al (2016) Overview of SHVC: scalable extensions of the high efficiency video coding standard. IEEE Transactions on Circuits and Systems for Video Technology 26(1):20–34

    Article  Google Scholar 

  4. Chandler DM, Hemami SS (2007) VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans Image Process 16(9):2284–2298

    Article  MathSciNet  Google Scholar 

  5. Chen Z, Guillemot C (2010) Perceptually-friendly H. 264/AVC video coding based on foveated just-noticeable-distortion model. IEEE Transactions on Circuits and Systems for Video Technology 20(6):806–819

    Article  Google Scholar 

  6. Chou CH, Chen CW (1996) A perceptually optimized 3-D subband codec for video communication over wireless channels. IEEE Transactions on Circuits and Systems for Video Technology 6(2):143–156

    Article  Google Scholar 

  7. Chou CH, Li YC (1995) A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile. IEEE Transactions on Circuits and Systems for Video Technology 5(6):467–476

    Article  Google Scholar 

  8. A. Hassan and A. Haseeb, “Rate-distortion modelling of scalable video sequences with different complexities,” IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, pp. 1–6, 2018.

  9. Hu S, Wang H, Kwong S, Kuo CCJ (2012) Novel rate-quantization model-based rate control with adaptive initialization for spatial scalable video coding. IEEE Trans Ind Electron 59(3):1673–1684

    Article  Google Scholar 

  10. ISO/IEC JTC1/SC29/WG11, “MPEG-4 video verification model v18.0,” Jan. 2001.

  11. ITU-T/SG15, “Video codec test model, TMN8,” June 1997.

  12. ITU-T, “Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment,” ITU-T P-Series Recommendations, P.913, 2016.

  13. Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process 13(10):1304–1318

    Article  Google Scholar 

  14. Joint Scalable Video Model JSVM 9–19-14 Software Package, CVS server for the JSVM software, 14 June 2011.

  15. Lee S, Pattichis MS, Bovik AC (2002) Foveated video quality assessment. IEEE Transactions on Multimedia 4(1):129–132

    Article  Google Scholar 

  16. Liu Y, Li ZG, Soh YC (2008) Rate control of H. 264/AVC scalable extension. IEEE Transactions on Circuits and Systems for Video Technology 18(1):116–121

    Article  Google Scholar 

  17. Luo Z, Song L, Zheng S, Ling N (2013) H. 264/advanced video control perceptual optimization coding based on JND-directed coefficient suppression. IEEE Transactions on Circuits and Systems for Video Technology 23(6):935–948

    Article  Google Scholar 

  18. Müller K, Schwarz H, Eisert P, Wiegand T (2019) Video Data Processing. In: Neugebauer R (ed) Digital transformation. Springer Vieweg, Berlin, Heidelberg

    Google Scholar 

  19. K. Nihei, H. Yoshida, N. Kai, K. Satoda, K. Chono, “Adaptive bitrate control of scalable video for live video streaming on best-effort network, ” IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, 9–13 Dec. 2018.

    Google Scholar 

  20. Reichel, J., Schwarz, H., Wien, M., and Vieron, J., “Joint Scalable Video Model 9 of ISO/IEC 14496–10:2005/AMC3 Scalable Video Coding,” document JVT-X202, Joint Video Team (JVT) of ISO-IEC MPEG and ITU-T VCEG, Geneva, Switzerland, Jul. 2007.

  21. Rezaei M, Karimghasemi E (2017) A fuzzy rate controller for variable bit rate video using foveated just-noticeable distortion model. Multimed Tools Appl 76(1):1439

    Article  Google Scholar 

  22. Rezaei M, Hannuksela MM, Gabbouj M (2008) Semi-fuzzy rate controller for variable bit rate video. IEEE Transactions on Circuits and Systems for Video Technology 18(5):633–645

    Article  Google Scholar 

  23. Ribas-Corbera J, Chou P, Regunathan SL (2003) A generalized hypothetical reference decoder for H. 264/AVC. IEEE Transactions on Circuits and Systems for Video Technology 13(7):674–687

    Article  Google Scholar 

  24. Samiee, A., Rezaei, M. and Shojaei, M., “Fuzzy aggregated rate controller for H.264/scalable video coding with dependent layer quantization,“4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 1–5, 2015.

  25. Schwarz H, Marpe D, Wiegand T (2007) Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Transactions on Circuits and Systems for Video Technology 17(9):1103–1120

    Article  Google Scholar 

  26. Sanz-Rodríguez, S., Díaz-de-María, F., and Rezaei, M., “Low-complexity VBR controller for spatial-CGS and temporal scalable video coding,” Picture Coding Symposium (PCS), IEEE, pp. 1–4, 2009.

  27. Sanz-Rodríguez, S., and Díaz-de-María, F., “RBF-based QP estimation model for VBR control in H. 264/SVC,“IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 9, pp. 1263–1277, 2011.

  28. Sanz-Rodriguez, S., Mayer, T., Alvarez-Mesa, M., and Schierl, T., “A low-complexity parallel-friendly rate control algorithm for ultra-low delay high definition video coding,” IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–4, 2013.

  29. Shen L, Liu Z, Zhang Z (2013) A novel H. 264 rate control algorithm with consideration of visual attention. Multimed Tools Appl 63(3):709–727

    Article  Google Scholar 

  30. Shojaei, M., Rezaei, M. and Samiee, A., “An independent-layer fuzzy rate controller for streaming applications of H. 264/SVC standard,“4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 1–5, 2015.

  31. Sullivan, G., Wiegand, T., Lim, K. P., “Joint model reference encoding methods and decoding concealment methods,“JVT-I049, Sep. 2003.

  32. Sullivan GJ et al (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on Circuits and Systems for Video Technology 22(12):1649–1668

    Article  Google Scholar 

  33. Wang LX (1999) A course in fuzzy systems and control. Prentice-Hall Press, USA

    Google Scholar 

  34. Wang X, Kwong S, Xu L, Zhang Y (2014) Generalized Nash bargaining solution to rate control optimization for spatial scalable video coding. IEEE Trans Image Process 23(9):4010–4021

    Article  MathSciNet  Google Scholar 

  35. Yuan D, Zhao T, Xu Y, Xue H, Lin L (2019) Visual JND: a perceptual measurement in video coding. IEEE Access 7:29014–29022

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehdi Rezaei.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shojaei, M., Rezaei, M. FJND-based fuzzy rate control of scalable video for streaming applications. Multimed Tools Appl 79, 13753–13773 (2020). https://doi.org/10.1007/s11042-019-08563-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08563-4

Keywords

Navigation