Skip to main content

Advertisement

SpringerLink
Log in

Bursty interference-oriented video quality assessment method

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

Abstract

The frequent bursty interference leads to wireless throughput variability in future networks, which results in video quality of experience (QoE) degradation. It is highly desirable to be able to predict video quality to meet QoE requirements. There has been a great deal of studies on video quality assessment, but only limited work has been reported for assessing video quality under bursty interference environment. In this paper, we seek to ameliorate this by developing a bursty interference-oriented video quality assessment algorithm. First, a subjective experiment has been carried out and a hysteresis model was proposed by analyzing the experiment data. Simulation result shows that in burst traffic environment, the model has a better correlation with human visual system (HVS) effect. Then we proposed an objective quality assessment algorithm by taking the video color, brightness, motion and other spatial features together with Structural Similarity Index Measurement (SSIM) into consideration, which outperforms Peak Signal Noise Rate (PSNR), Visual Information Fidelity (VIF) and SSIM in bursty environment.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ansgar KR, Li Z Feature-specific interactions in salience from combined feature contrasts: Evidence for a bottom{up saliency map in v1. J of Vision 7(7):6

  2. BT, RECOMMENDATION ITU-R (2002) Methodology for the subjective assessment of the quality of television pictures, International Telecommunication Union

  3. Carmi R, Itti L (2006) Visual causes versus correlates of attentional selection in dynamic scenes. Vis Res 46(26):4333–4345

    Article  Google Scholar 

  4. Cisco (2013) Cisco visual networking index: global mobile data traffic forecast update, 2012–2017. [Online]. Available: http://goo.gl/xxLT

  5. Culibrk D, Crnojevic V, Antic B (2009) Multiscale background modelling and segmentation. IEEE Int Conf Digital Signal Process 1–6

  6. Damnjanovic A, Montojo J, Wei Y, Ji T, Luo T, Vajapeyam M, Yoo T, Song O, Malladi D (2011) A survey on 3gpp heterogeneous networks. IEEE Wirel Commun 18(3):10–21

    Article  Google Scholar 

  7. De Simone F, Naccari M, Tagliasacchi M, Dufaux F, Tubaro S, Ebrahimi T (2009) Subjective assessment of h. 264/avc video sequences transmitted over a noisy channel. In: Int Workshop on Quality of Multimedia Experience, IEEE 204–209

  8. Engelke U, Kaprykowsky H, Zepernick H, Ndjiki-Nya P (2011) Visual attention in quality assessment. IEEE Signal Process Mag 28(6):50–59

    Article  Google Scholar 

  9. Engelke U, Zepernick H, Maeder A (2009) Visual attention modeling: region-of-interest versus fixation patterns. Picture Coding Symp 1–4

  10. Fu B, Lu Z, Wen X, Wang L, Shao H (2013) Visual attention modeling for video quality assessment with structural similarity. IEEE Int Symp Wirel Pers Multimedia Commun (WPMC) 1–5

  11. Hou T, Wang S, Qin H (2011) Image deconvolution with multi-stage convex relaxation and its perceptual evaluation. IEEE Trans Image Process 20(12):3383–3392

    Article  MathSciNet  Google Scholar 

  12. Inazumi Y, Horita Y, Kotani K, Murai T (1999) Quality evaluation method considering time transition of coded video quality. IEEE Int Conf Image Process 4:338–342

    Google Scholar 

  13. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259

    Article  Google Scholar 

  14. Lee C, Kwon O (2003) Objective measurements of video quality using the wavelet transform. Opt Eng 42(1):265–272

    Article  MathSciNet  Google Scholar 

  15. Li L, Huang W, Gu IH, Tian Q (2004) Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process 13(11):1459–1472

    Article  Google Scholar 

  16. Li S, Ma L, Ngan KN (2012) Full-reference video quality assessment by decoupling detail losses and additive impairments. IEEE Trans Circuits Syst Video Technol 22(7):1100–1112

    Article  Google Scholar 

  17. Lu X, Dang S (2010) A new animation image color-texture feature extraction method. IEEE Int Conf Multimedia Technol (ICMT) 1–4

  18. Ma L, Lin W, Deng C, Ngan KN (2012) Image retargeting quality assessment: a study of subjective scores and objective metrics. IEEE J Sel Topics Signal Process 6(6):626–639

    Article  Google Scholar 

  19. Ma L, Lin W, Deng C, Ngan KN (2012b) Study of subjective and objective quality assessment of retargeted images. IEEE Int Symp Circ Syst (ISCAS) 2677–2680

  20. Masry MA, Hemami SS (2004) A metric for continuous quality evaluation of compressed video with severe distortions. Signal Process Image Commun 19(2):133–146

    Article  Google Scholar 

  21. Moorthy AK, Seshadrinathan K, Soundararajan R, Bovik AC (2010) Wireless video quality assessment: A study of subjective scores and objective algorithms. IEEE Trans Circuits Syst Video Technol 20(4):587–599

    Article  Google Scholar 

  22. Pappas TN, Safranek RJ, Chen J (2000) Perceptual criteria for image quality evaluation. Handbook of image and video process 669–684

  23. Rensink RA (2000) Seeing, sensing, and scrutinizing. Vis Res 40(10):1469–1487

    Article  Google Scholar 

  24. Seshadrinathan K, Bovik AC (2008) Unifying analysis of full reference image quality assessment. IEEE Int Conf Image Process IEEE 1200–1203

  25. Seshadrinathan K, Bovik AC (2009) Motion-based perceptual quality assessment of video. SPIE Electron Imaging Int Soc Opt Photon 72,400X-72,400X

  26. Seshadrinathan K, Bovik AC (2011) Temporal hysteresis model of time varying subjective video quality. IEEE Int Conf Acoust Speech Signal Process (ICASSP) 1153–1156

  27. Seshadrinathan K, Soundararajan R, Bovik AC, Cormack LK (2010) Study of subjective and objective quality assessment of video[J]. IEEE Trans Image Process 19(6):1427–1441

    Article  MathSciNet  Google Scholar 

  28. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Article  Google Scholar 

  29. Singh S, Andrews JG, de Veciana G (2012) Interference shaping for improved quality of experience for real-time video streaming. IEEE J Sel Areas Commun 30(7):1259–1269

    Article  Google Scholar 

  30. Song X, Yang Y (2009) A new no-reference assessment metric of blocking artifacts based on hvs masking effect. In: 2009 2nd Int. Congress on Image and Signal Processing 1–6

  31. Tan K, Ghanbari M, Pearson DE (1998) An objective measurement tool for mpeg video quality. Signal Process 70(3):279–294

    Article  MATH  Google Scholar 

  32. Taylor CN, Dey S (2004) Runtime allocation of buffer resources for maximizing video clip quality in a wireless last-hop system. IEEE Int Conf Commun 5:3081–3085

    Google Scholar 

  33. Telefon AB LM Ericsson, ST-Ericsson SA, QoE for HTTP streaming, TdocS4-AHI118, Online available at: http://www.3gpp.org/ftp/tsg_sa/WG4_CODEC/Ad-hoc_MBS/Docs_AHI/S4-AHI118.zip.

  34. Tom MD, Tenorio MF (1995) A neural computation model with short-term memory. IEEE Trans Neural Netw 6(2):387–397

    Article  Google Scholar 

  35. Tsotsos JK, Liu Y, Martinez-Trujillo JC, Pomplun M, Simine E, Zhou K (2005) Attending to visual motion. Comput Vis Image Underst 100:3–40

    Article  Google Scholar 

  36. VQEG (2003) Final Report from the video quality experts group on the validation of objective models of video quality assessment, Phase II (FR-TV 2)

  37. Wandell BA (1995) Foundations of vision. Sinauer, Sunderland

    Google Scholar 

  38. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  39. Wang Z, Lu LG, Bovik AC (2002) Video quality assessment using structural distortion measurement[J]. Int Conf Image Process 3(24–28):65–68

    Google Scholar 

  40. Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: IEEE Conf. Record of the Thirty-Seventh Asilomar Conf. on Signals, Syst. and Comput 2: 1398-tso1402.

  41. Watson AB (1993) Dctune: A technique for visual optimization of dct quantization matrices for individual images. Sid Int Symp Digest Tech Papers Soc Inf Disp 24:946

    Google Scholar 

  42. You J, Ebrahimi T, Perkis A (2014) Attention driven foveated video quality assessment. IEEE Trans Image Process 23(1):200–213

    Article  MathSciNet  Google Scholar 

  43. Zhao Y, Yu L, Chen Z, Zhu C (2011) Video quality assessment based on measuring perceptual noise from spatial and temporal perspectives. IEEE Trans Circuits Syst Video Technol 21(12):1890–1902

    Article  Google Scholar 

  44. Zink M, Kunzel O, Schmitt J, Steinmetz R (2003) Subjective impression of variations in layer encoded videos. In: Quality of Service-IWQoS 2003, Springer, 137–154

Download references

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China

    Shiyu Zhou, Zhaoming Lu, Xiangming Wen, Bin Fu, Hua Shao & Yawen Chen

  2. Research Department of Network Optimization and Management Technology, Network Technology Research Institute, China United Network Communications Corporation Limited, Beijing, 100876, People’s Republic of China

    Shiyu Zhou

Authors
  1. Shiyu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoming Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangming Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hua Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yawen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoming Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, S., Lu, Z., Wen, X. et al. Bursty interference-oriented video quality assessment method. Multimed Tools Appl 75, 2741–2768 (2016). https://doi.org/10.1007/s11042-015-2787-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2787-y

Keywords

Search

Navigation