Real-time no-reference histogram-based freezing artifact detection algorithm for UHD videos


During video transmission different errors can occur, which can introduce distinct artifacts in video received at the end user side. One of the most common artifacts that appears in such cases is frame freezing and it can significantly reduce the quality of received video, i.e. decrease the level of end user Quality of Experience (QoE). Thus it is necessary to properly detect the occurrence of frame freezing, in order to assure the satisfactory level of end user QoE. In this paper a novel no-reference (NR) objective algorithm for detection of various types of freezing artifacts in video, called Histogram-Based Freezing artifacts Detection Algorithm (HBFDA), is proposed. HBFDA uses method for comparison of consecutive video frames, which consists of splitting frame into regions and comparing regions’ histograms of consecutive frames. In order to operate on different types of video contents (including those containing a low level of movement), while achieving high level of accuracy and reliability, HBFDA dynamically adapts its parameters in real-time. HBFDA performance are compared to this of two freely pulbicly available algorithms for frame freezing detection on videos from three different databases: VQEG HDTV, LIVE Mobile and newly created UHD Video Freezing Database (UHD VFD), which we made publicly available to the research community and it can be downloaded at Experimental results show that HBFDA has lowest number of false freezing detections when compared to other tested algorithms and that it achieves the highest average total accuracy of 99.26% when all databases are taken into account. Additionally, for UHD videos, HBFDA is able to process 75 frames per second, which makes it suitable for real-time video applications.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Asha V, Bhajantri NU, Nagabhushan P (2011) GLCM–based chi–square histogram distance for automatic detection of defects on patterned textures. Int J Comput Vis Robot 2:302–313

    Article  Google Scholar 

  2. 2.

    AWS Elemental - 4K Test Sequences (2017) In: AWS Elem. - 4K Test Seq. Accessed 28 Apr 2017

  3. 3.

    Baldi M, Ofek Y (2000) End-to-end delay analysis of videoconferencing over packet-switched networks. IEEEACM Trans Netw 8:479–492.

    Article  Google Scholar 

  4. 4.

    Borer S (2010) A model of jerkiness for temporal impairments in Video transmission. In: 2010 second international workshop on quality of multimedia experience (QoMEX). IEEE: 218–223

  5. 5.

    Dagum L, Menon R (1998) OpenMP: an industry standard API for shared-memory programming. IEEE Comput Sci Eng 5:46–55.

    Article  Google Scholar 

  6. 6.

    FFmpeg (2018) Accessed 3 Feb 2018

  7. 7.

    Huynh-Thu Q, Ghanbari M (2008) Temporal aspect of perceived quality in Mobile Video broadcasting. IEEE Trans Broadcast 54:641–651.

    Article  Google Scholar 

  8. 8.

    Huynh-Thu Q, Ghanbari M (2009) No-reference temporal quality metric for video impaired by frame freezing artefacts. Proc - Int Conf Image Process ICIP: 2221–2224. doi:

  9. 9.

    International Telecommunication Union (2008) ITU-T recommendation P.910, Subjective Video quality assessment methods for multimedia applications

  10. 10.

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

    Article  Google Scholar 

  11. 11.

    van Kester S, Xiao T, Kooij RE, et al (2011) Estimating the impact of single and multiple freezes on Video quality. SPIE Proc. Int Soc Optics Photo

  12. 12.

    Lamb AB, Khambete M (2018) No-reference perceived image quality measurement for multiple distortions. Multimed Tools Appl 77:8653–8675.

    Article  Google Scholar 

  13. 13.

    Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2002) A no-reference perceptual blur metric. Proceedings. International Conference on Image Processing

  14. 14.

    Mittal A, Saad MA, Bovik AC (2016) A completely blind Video integrity Oracle. IEEE Trans Image Process 25:289–300.

    MathSciNet  Article  MATH  Google Scholar 

  15. 15.

    Moorthy AK, Choi LK, Bovik AC, de Veciana G (2012) Video quality assessment on Mobile devices: subjective, behavioral and objective studies. IEEE J Sel Top Signal Process 6:652–671.

    Article  Google Scholar 

  16. 16.

    Moorthy AK, Choi LK, De Veciana G, Bovik A (2012) Mobile Video quality assessment database. In: IEEE ICC workshop on realizing advanced Video optimized Wirel Netw: 652–671

  17. 17.

    Moorthy AK, Choi LK, De Veciana G, Bovik AC (2012) Subjective analysis of video quality on mobile devices. Sixth International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), Scottsdale, Arizona

  18. 18.

    Muijs R, Kirenko I (2005) A no-reference blocking artifact measure for adaptive video processing. In: 2005 13th European signal processing conference: 1–4

  19. 19.

    OpenCV (2018) Histogram Comparison. Accessed 30 Jan 2018

  20. 20.

    OpenCV (2018) Accessed 21 Apr 2018

  21. 21.

    Pastrana-Vidal RR, Gicquel JC, Colomes C, Cherifi H (2004) Sporadic frame dropping impact on quality perception. In: human vision and electronic imaging IX. International Society for Optics and Photonics: 182–194

  22. 22.

    Qi Y, Dai M (2006) The effect of frame freezing and frame skipping on video quality. In: intelligent information hiding and multimedia signal processing, 2006. IIH-MSP’06. International conference on. IEEE: 423–426

  23. 23.

    Ruderman DL (1994) The statistics of natural images. Netw Comput Neural Syst 5:517–548.

    Article  MATH  Google Scholar 

  24. 24.

    Shahid M, Rossholm A, Lövström B, Zepernick H-J (2014) No-reference image and Video quality assessment: a classification and review of recent approaches. EURASIP J Image Video Process 2014:40

    Article  Google Scholar 

  25. 25.

    Škobić V, Marić U, Tomić S, Rešetar I (2016) Real time Video freezing detection implementation on FPGA. In: telecommunications forum (TELFOR), 2016. IEEE: 1–3

  26. 26.

    Teslic N, Zlokolica V, Pekovic V et al (2010) Packet-loss error detection system for DTV and set-top box functional testing. IEEE Trans Consum Electron 56:1311–1319.

    Article  Google Scholar 

  27. 27.

    Ultra Video Group (2018) Accessed 30 Jan 2018

  28. 28.

    Usman MA, Usman MR, Shin SY (2015) The impact of temporal impairment on quality of experience (QOE) in Video streaming: a no reference (NR) subjective and objective study. Int J Comput Electr Autom Control Inf Eng 9:1570–1577

    Google Scholar 

  29. 29.

    Usman MA, Usman MR, Shin SY (2016) A no reference method for detection of dropped Video frames in live Video streaming. In: eighth international conference on ubiquitous and future networks (ICUFN). IEEE: 839–844

  30. 30.

    Usman MA, Shin SY, Shahid M, Lövström B (2016) A no reference Video quality metric based on jerkiness estimation focusing on multiple frame freezing in Video streaming. IETE Tech Rev 1–12. doi:

  31. 31.

    Usman MA, Usman MR, Shin SY (2018) A novel no-reference metric for estimating the impact of frame freezing artifacts on perceptual quality of streamed videos. IEEE Trans Multimed 1–1. doi:

  32. 32.

    Video Quality Experts Group (2010) Report on the validation of video quality models for high definition video content

  33. 33.

    Vranješ M, Rimac-Drlje S, Grgić K (2013) Review of objective Video quality metrics and performance comparison using different databases. Signal Process Image Commun 28:1–19.

    Article  Google Scholar 

  34. 34.

    Wolf S (2009) A no reference (NR) and reduced reference (RR) metric for detecting dropped video frames. Fourth Int workshop VIDEO process Qual Metr Consum Electron 15–16

  35. 35.

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

    Article  Google Scholar 

  36. 36.

    Yammine G, Wige E, Simmet F et al (2012) Blind frame freeze detection in coded videos. 2012 Pict coding Symp PCS 2012 proc 341–344. doi:

  37. 37.

    Zhao S, Jiang H, Liang C, Sherif S (2016) Mathematical models for quality analysis of Mobile Video. Int J Numer Anal Model 13:879–897

    MathSciNet  Google Scholar 

  38. 38.

    Zlokolica V, Pekovic V, Teslic N, et al (2011) Video freezing detection system for end-user devices. Dig Tech Pap - IEEE Int Conf Consum Electron 81–82. doi:

Download references


This work was supported by the Josip Juraj Strossmayer University of Osijek IZIP-2016, via the project “Providing of digital video signal based services in rural and rarely populated areas” and by the Ministry of Education, Science and Technological Development of the Republic of Serbia, under grant number III_044009_6.

Author information



Corresponding author

Correspondence to Mario Vranješ.

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

Verify currency and authenticity via CrossMark

Cite this article

Babić, D., Stefanović, D., Vranješ, M. et al. Real-time no-reference histogram-based freezing artifact detection algorithm for UHD videos. Multimed Tools Appl 78, 17949–17971 (2019).

Download citation


  • Artifact
  • Freezing
  • Histogram
  • No-reference
  • Real-time
  • UHD