Skip to main content


We're creating a new version of this page. See preview

  • Research Article
  • Open Access

A Method for Single-Stimulus Quality Assessment of Segmented Video

EURASIP Journal on Advances in Signal Processing20062006:039482

  • Received: 17 March 2005
  • Accepted: 31 July 2005
  • Published:


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.


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

Authors’ Affiliations

Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK
Centre for Vision, Speech and Signal Processing (CVSSP), School of Electronics and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK


  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. 10.1109/76.809153View ArticleGoogle Scholar
  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/BF01201137View ArticleGoogle Scholar
  3. Zhang YJ: A survey on evaluation methods for image segmentation. Pattern Recognition 1996, 29(8):1335–1346. 10.1016/0031-3203(95)00169-7View ArticleGoogle Scholar
  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-XView ArticleGoogle Scholar
  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-XView ArticleGoogle Scholar
  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)00026-1View ArticleGoogle Scholar
  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.View ArticleGoogle Scholar
  8. International Telecommunication Union ITU.R BT.500-11: Methodology for the subjective assessment of the quality of television pictures, 2002Google Scholar
  9. International Telecommunication Union. ITU.R BT.710-4: Subjective assessment methods for image quality in high-definition television, 1998Google Scholar
  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. 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.2004.823398View ArticleGoogle Scholar
  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. 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.2004.828427View ArticleGoogle Scholar
  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. 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.View ArticleGoogle Scholar
  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. 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.2004.828433View ArticleGoogle Scholar
  18. Correia PL, Pereira F: Objective evaluation of video segmentation quality. IEEE Trans. Image Processing 2003, 12(2):186–200. 10.1109/TIP.2002.807355View ArticleGoogle Scholar
  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.2004.826778View ArticleGoogle Scholar
  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. 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.View ArticleGoogle Scholar
  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. 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. 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. 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.View ArticleGoogle Scholar
  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.MATHGoogle Scholar
  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.2002.800885MathSciNetView ArticleGoogle Scholar
  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.1999.0825View ArticleGoogle Scholar
  29. Piroddi R: Multiple-feature object-based segmentation of video sequences. Centre for Vision, Speech and Signal Processing, University of Surrey, 2004Google Scholar
  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), 2001Google Scholar
  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. 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.View ArticleGoogle Scholar
  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. 10.1109/76.564107View ArticleGoogle Scholar
  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. 10.1109/76.735378View ArticleGoogle Scholar


© Piroddi and Vlachos 2006