Skip to main content

A Method for Single-Stimulus Quality Assessment of Segmented Video

Abstract

We present a unified method for single-stimulus quality assessment of segmented video. This method takes into consideration colour and motion features of a moving sequence and monitors their changes across segment boundaries. Features are estimated using a local neighbourhood which preserves the topological integrity of segment boundaries. Furthermore the proposed method addresses the problem of unreliable and/or unavailable feature estimates by applying normalized differential convolution (NDC). Our experimental results suggest that the proposed method outperforms competing methods in terms of sensitivity as well as noise immunity for a variety of standard test sequences.

References

  1. 1.

    Salembier P, Marques F: Region-based representations of image and video: segmentation tools for multimedia services. IEEE Trans. Circuits Syst. Video Technol. 1999, 9(8):1147–1169. 10.1109/76.809153

    Article  Google Scholar 

  2. 2.

    Zhang D, Lu G: Segmentation of moving objects in image sequence: a review. Circuits Systems and Signal Processing 2001, 20(2):143–183. 10.1007/BF01201137

    Article  Google Scholar 

  3. 3.

    Zhang YJ: A survey on evaluation methods for image segmentation. Pattern Recognition 1996, 29(8):1335–1346. 10.1016/0031-3203(95)00169-7

    Article  Google Scholar 

  4. 4.

    Borsotti M, Campadelli P, Schettini R: Quantitative evaluation of color image segmentation results. Pattern Recognition Letters 1998, 19(8):741–747. 10.1016/S0167-8655(98)00052-X

    Article  Google Scholar 

  5. 5.

    Zhang X, Wandell BA: Color image fidelity metrics evaluated using image distortion maps. Signal Processing 1998, 70(3):201–214. 10.1016/S0165-1684(98)00125-X

    Article  Google Scholar 

  6. 6.

    van Dijk AM, Martens J-B: Subjective quality assessment of compressed images. Signal Processing 1997, 58(3):235–252. 10.1016/S0165-1684(97)00026-1

    Article  Google Scholar 

  7. 7.

    Meesters LMJ, IJsselsteijn WA, Seuntiens PJH: A survey of perceptual quality issues in three-dimensional television systems. Stereoscopic Displays and Virtual Reality Systems X, January 2003, Santa Clara, Calif, USA, Proceedings of SPIE 5006: 313–326.

    Article  Google Scholar 

  8. 8.

    International Telecommunication Union ITU.R BT.500-11: Methodology for the subjective assessment of the quality of television pictures, 2002

  9. 9.

    International Telecommunication Union. ITU.R BT.710-4: Subjective assessment methods for image quality in high-definition television, 1998

  10. 10.

    Pinson MH, Wolf S: Comparing subjective video quality testing methodologies. Visual Communications and Image Processing (VCIP '03), July 2003, Lugano, Switzerland, Proceedings of SPIE 5150: 573–582.

    Google Scholar 

  11. 11.

    Meesters LMJ, IJsselsteijn WA, Seuntiens PJH: A survey of perceptual evaluations and requirements of three-dimensional TV. IEEE Trans. Circuits Syst. Video Technol. 2004, 14(3):381–391. 10.1109/TCSVT.2004.823398

    Article  Google Scholar 

  12. 12.

    Cavallaro A, Ebrahimi T: Object-based video: extraction tools, evaluation metrics, and applications. Visual Communications and Image Processing (VCIP '03), July 2003, Lugano, Switzerland, Proceedings of SPIE 5150: 1–8.

    Google Scholar 

  13. 13.

    Erdem CE, Sankur B, Tekalp AM: Performance measures for video object segmentation and tracking. IEEE Trans. Image Processing 2004, 13(7):937–951. 10.1109/TIP.2004.828427

    Article  Google Scholar 

  14. 14.

    Erdem CE, Sankur B: Performance evaluation metrics for object-based video segmentation. Proc. 11th European Signal Processing Conference (EUSIPCO '02), September 2002, Toulouse, France 2: 917–920.

    Google Scholar 

  15. 15.

    Correia P, Pereira F: Objective evaluation of relative segmentation quality. Proc. IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, BC, Canada 1: 308–311.

    Article  Google Scholar 

  16. 16.

    Cavallaro A, Gelasca ED, Ebrahimi T: Objective evaluation of segmentation quality using spatio-temporal context. Proc. IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 3: 301–304.

    Google Scholar 

  17. 17.

    Villegas P, Marichal X: Perceptually-weighted evaluation criteria for segmentation masks in video sequences. IEEE Trans. Image Processing 2004, 13(8):1092–1103. 10.1109/TIP.2004.828433

    Article  Google Scholar 

  18. 18.

    Correia PL, Pereira F: Objective evaluation of video segmentation quality. IEEE Trans. Image Processing 2003, 12(2):186–200. 10.1109/TIP.2002.807355

    Article  Google Scholar 

  19. 19.

    Correia PL, Pereira F: Classification of video segmentation application scenarios. IEEE Trans. Circuits Syst. Video Technol. 2004, 14(5):735–741. 10.1109/TCSVT.2004.826778

    Article  Google Scholar 

  20. 20.

    Erdem CE, Tekalp AM, Sankur B: Metrics for performance evaluation of video object segmentation and tracking without ground-truth. Proc. IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 69–72.

    Google Scholar 

  21. 21.

    Knutsson H, Westin C-F: Normalized and differential convolution: methods for interpolation and filtering of incomplete and uncertain data. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR '93), June 1993, New York, NY, USA 515–523.

    Google Scholar 

  22. 22.

    Westin C-F, Nordberg K, Knutsson H: On the equivalence of normalized convolution and normalized differential convolution. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '94), April 1994, Adelaide, SA, Australia 5: 457–460.

    Google Scholar 

  23. 23.

    Westin C-F, Knutsson H: Processing incomplete and uncertain data using subspace methods. Proc. 12th IAPR International Conference on Pattern Recognition, October 1994, Jerusalem, Israel 3: 171–173.

    Google Scholar 

  24. 24.

    Piroddi R, Petrou M: Dealing with irregular samples. In Advances in Imaging and Electron Physics. Volume 132. Edited by: Hawkes PW. Elsevier, Amsterdam, The Netherlands; 2004:109–165.

    Google Scholar 

  25. 25.

    Petrou M, Piroddi R, Chandra S: Irregularly Sampled Scenes. Image and Signal Processing for Remote Sensing X, September 2004, Maspalomas, Gran Canaria, Spain, Proceedings of SPIE 5573: 319–333.

    Article  Google Scholar 

  26. 26.

    Pham TQ, van Vliet LJ: Normalized averaging using adaptive applicability functions with applications in image reconstruction from sparsely and randomly sampled data. Proc. 13th Scandinavian Conference on Image Analysis (SCIA '03), June–July 2003, Halmstad, Sweden, Lecture Notes in Computer Science 2749: 485–492.

    MATH  Google Scholar 

  27. 27.

    Rieger B, van Vliet LJ:Curvature of -dimensional space curves in grey-value images. IEEE Trans. Image Processing 2002, 11(7):738–745. 10.1109/TIP.2002.800885

    MathSciNet  Article  Google Scholar 

  28. 28.

    Black MJ, Fleet DJ, Yacoob Y: Robustly estimating changes in image appearance. Computer Vision and Image Understanding 2000, 78(1):8–31. 10.1006/cviu.1999.0825

    Article  Google Scholar 

  29. 29.

    Piroddi R: Multiple-feature object-based segmentation of video sequences. Centre for Vision, Speech and Signal Processing, University of Surrey, 2004

    Google Scholar 

  30. 30.

    Koenen R: From MPEG-1 to MPEG-21: creating an interoperable multimedia infrastructure. International Organisation for Standardisation—Organisation Internationale de Normalisation ISO/IEC JTC1/SC29/WG11 (Coding of Moving Pictures and audio), 2001

    Google Scholar 

  31. 31.

    Piroddi R, Vlachos T: Multiple-feature segmentation of moving sequences using a rule-based approach. Proc. 13th British Machine Vision Conference (BMVC '02), September 2002, Cardiff , UK 1: 353–362.

    Google Scholar 

  32. 32.

    Alatan AA, Tuncel E, Onural L: A rule-based method for object segmentation in video sequences. Proc. IEEE International Conference on Image Processing (ICIP '97), October 1997, Santa Barbara, Calif, USA 2: 522–525.

    Article  Google Scholar 

  33. 33.

    Choi JG, Lee S-W, Kim S-D: Spatio-temporal video segmentation using a joint similarity measure. IEEE Trans. Circuits Syst. Video Technol. 1997, 7(2):279–286. 10.1109/76.564107

    Article  Google Scholar 

  34. 34.

    Alatan AA, Onural, L, Wollborn M, Mech R, Tuncel E, Sikora T: Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework. IEEE Trans. Circuits Syst. Video Technol. 1998, 8(7):802–813. 10.1109/76.735378

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to R Piroddi.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Piroddi, R., Vlachos, T. A Method for Single-Stimulus Quality Assessment of Segmented Video. EURASIP J. Adv. Signal Process. 2006, 039482 (2006). https://doi.org/10.1155/ASP/2006/39482

Download citation

Keywords

  • Colour
  • Information Technology
  • Convolution
  • Quality Assessment
  • Standard Test