Skip to main content
Log in

RT-BVE—Real-Time no-reference Blocking Visibility Estimation in video frames

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

Abstract

During lossy video compression, a blocking artifact is introduced into a video. It can significantly reduce the quality of a compressed video, and consequently end-user Quality of Experience. Hence, it is very important to measure and monitor the quality of compressed videos delivered to the end-user, which can be performed only by using the no-reference (NR) approach, without the original video signal available. Many NR video quality assessment (VQA) metrics are based on measurements of different artifacts visibility, which are then used as an input for an objective NR VQA metric. Thus, reliable artifacts detection and their visibility estimation are the first step in a reliable VQA process. In this paper, a new algorithm for Real-Time NR Blocking Visibility Estimation (RT-BVE) in video frames is proposed. The performance of the proposed RT-BVE algorithm is compared to the performance of two freely available algorithms, i.e., BBT-BDA and MSU Blocking, using videos from the well-known databases (156 video sequences in total and more than 48,000 frames). While RT-BVE achieves performance comparable to BBT-BDA and MSU Blocking algorithms for MPEG-2 videos, it outperforms both algorithms for H.264 videos. The results show that the proposed RT-BVE algorithm can precisely estimate blocking visibility in video frames for videos compressed according to different compression standards by using a fixed set of algorithm parameter values. Additionally, RT-BVE is suitable for usage in real-time applications since it is capable of processing approximately 100 Full HD video frames per second on the current midrange × 86–64 platform.

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

Similar content being viewed by others

References

  1. Liang, L., Wang, S., Chen, J., Ma, S., Zhao, D., Gao, W.: No-reference perceptual image quality metric using gradient profiles for JPEG2000. Signal Process. Image Commun. 25(7), 502–516 (2010)

    Article  Google Scholar 

  2. Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50(3), 312–322 (2004)

    Article  Google Scholar 

  3. Salah, A., Gutub, A.: Intelligent recognition system for identifying items and pilgrims. NED Univ. J. Res. 15(2), 17–23 (2018)

    Google Scholar 

  4. Abdelgawad, H., Shalaby, A., Abdulhai, B., Gutub, A.: Microscopic modeling of large-scale pedestrian–vehicle conflicts in the city of Madinah, Saudi Arabia. J. Adv. Transp. 48(6), 507–525 (2014)

    Article  Google Scholar 

  5. Mittal, A., Moorthy, A., Bovik, A.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  6. Joshi, P., Prakash, S.: Retina inspired no-reference image quality assessment for blur and noise. Multimed. Tools Appl. 76(18), 18871–18890 (2017)

    Article  Google Scholar 

  7. Shen, L., Lei, J., Hou, C.: No-reference stereoscopic 3D image quality assessment via combined model. Multimed. Tools Appl. 77(7), 8195–8212 (2018)

    Article  Google Scholar 

  8. Saad, M.A., Bovik, A.C., Charrier, C.: Blind prediction of natural video quality. IEEE Trans. Image Process. 23(3), 1352–1365 (2014)

    Article  MathSciNet  Google Scholar 

  9. Zhu, K., Li, C., Asari, V., Saupe, D.: No-reference video quality assessment based on artifact measurement and statistical analysis. IEEE Trans. Circuits Syst. Video Technol. 25(4), 533–546 (2015)

    Article  Google Scholar 

  10. Li, X., Guo, Q., Lu, X.: Spatiotemporal statistics for video quality assessment. IEEE Trans. Image Process. 25(7), 3329–3342 (2016)

    Article  MathSciNet  Google Scholar 

  11. Søgaard, J., Forchhammer, S., Korhonen, J.: No-reference video quality assessment using codec analysis. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1637–1650 (2015)

    Article  Google Scholar 

  12. Xue, Y., Erkin, B., Wang, Y.: A novel no-reference video quality metric for evaluating temporal jerkiness due to frame freezing. IEEE Trans. Multimed. 17(1), 134–139 (2015)

    Article  Google Scholar 

  13. Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: Study of subjective and objective quality assessment of video. IEEE Trans. Image Process. 19(6), 1427–1441 (2010)

    Article  MathSciNet  Google Scholar 

  14. Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: A subjective study to evaluate video quality assessment algorithms. SPIE Proc. Hum. Vis. Electron. Imaging 7527, 75270 (2010)

    Article  Google Scholar 

  15. Bajčinovci, V., Vranješ, M., Babić, D., Kovačević, B.: Subjective and objective quality assessment of MPEG-2, H. 264 and H. 265 videos. In: 59th International Symposium ELMAR-2017 (Zadar, 18–20 September 2017), pp. 73–77 (2017)

  16. Wang, Z., Bovik, A.C., Evans, B.L.: Blind measurement of blocking artifacts in images. Proc. IEEE Int. Conf. Image Process. 3, 981–984 (2000)

    Article  Google Scholar 

  17. Perra, C.: A low computational complexity blockiness estimation based on spatial analysis. In: 22nd IEEE Telecommunications Forum Telfor (TELFOR) (Belgrade, 25–27 November 2014), pp. 1130–1133 (2014)

  18. Yammine, G., Wige E., Kaup, A.: A no-reference blocking artifacts visibility estimator in images. In: 2010 IEEE International Conference on Image Processing (Hong Kong, 2010), pp. 2497–2500 (2010)

  19. Lee, S., Park, S.J.: A new image quality assessment method to detect and measure strength of blocking artifacts. Signal Process. Image Commun. 27(1), 31–38 (2012)

    Article  Google Scholar 

  20. Singh, J., Singh, D., Uddin, M.: Detection methods for blocking artefacts in transform coded images. IET Image Proc. 8(8), 435–444 (2014)

    Article  Google Scholar 

  21. Zhang, Z., Shi, H., Wan, S.: A novel blind measurement of blocking artifacts for H.264/AVC video. In International Conference on Image and Graphics (Xian, 20–23 September 2009), pp. 262–265 (2009)

  22. Abate, L., Ramponi, G., Stessen, J.: Detection and measurement of the blocking artifact in decoded video frames. SIViP 7(3), 453–466 (2013)

    Article  Google Scholar 

  23. Deng, Y., Yang, Q., Lu, J., Liu, N., Qiao, Y., Sun, Y.: A hybrid no-reference blockiness metric for H. 264 standard. In 10th IEEE International Conference on Control and Automation (ICCA) (Hangzhou, 12–14 June 2013), pp. 136–1371 (2013)

  24. Amor, M.B., Larabi, M.C., Kammoun, F., Masmoudi, N.: A block artifact distortion measure for no reference video quality evaluation. In: 1st IEEE International Image Processing, Applications and Systems Conference (IPAS) (Hammamet, 5–7 November 2014), pp. 1–5 (2014)

  25. MSU Video Quality Measurement Tool. http://www.compression.ru/video/quality_measure/video_measurement_tool.html. (2019). Accessed 15 Dec 2019

  26. MSU Blocking Metric. http://www.compression.ru/video/quality_measure/info.html#blockingmeasure. (2019) Accessed 15 Dec 2019

  27. Institute RT-RK, BBT. https://www.rt-rk.com/products/testing-tools. (2019) Accessed 20 Dec 2019

Download references

Acknowledgements

This work was supported by J.J. Strossmayer University of Osijek through the internal competition for research and artistic projects via the project “UNIOS-ZUP 2018-6.” The authors would like to thank the VQMT support team for providing the results for the MSU Quality Measurement Tool.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mario Vranješ.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Jurić, T., Vranješ, M., Grbić, R. et al. RT-BVE—Real-Time no-reference Blocking Visibility Estimation in video frames. J Real-Time Image Proc 18, 1921–1936 (2021). https://doi.org/10.1007/s11554-020-01065-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-020-01065-9

Keywords

Navigation