A Method for Single-Stimulus Quality Assessment of Segmented Video

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.

[1]  Fernando Pereira,et al.  Objective evaluation of video segmentation quality , 2003, IEEE Trans. Image Process..

[2]  David J. Fleet,et al.  Robustly Estimating Changes in Image Appearance , 2000, Comput. Vis. Image Underst..

[3]  Wijnand A. IJsselsteijn,et al.  A survey of perceptual evaluations and requirements of three-dimensional TV , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Paola Campadelli,et al.  Quantitative evaluation of color image segmentation results , 1998, Pattern Recognit. Lett..

[5]  Margaret H. Pinson,et al.  Comparing subjective video quality testing methodologies , 2003, Visual Communications and Image Processing.

[6]  Fernando Pereira,et al.  Classification of video segmentation application scenarios , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Levent Onural,et al.  Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework , 1998, IEEE Trans. Circuits Syst. Video Technol..

[8]  Carl-Fredrik Westin,et al.  On the equivalence of normalized convolution and normalized differential convolution , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Bülent Sankur,et al.  Performance evaluation metrics for object-based video segmentation , 2000, 2000 10th European Signal Processing Conference.

[10]  C. Westin,et al.  Normalized and differential convolution , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Paulo Villegas,et al.  Perceptually-weighted evaluation criteria for segmentation masks in video sequences , 2004, IEEE Transactions on Image Processing.

[12]  Carl-Fredrik Westin,et al.  Processing incomplete and uncertain data using subspace methods , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[13]  Levent Onural,et al.  A rule-based method for object segmentation in video sequences , 1997, Proceedings of International Conference on Image Processing.

[14]  Lucas J. van Vliet,et al.  Curvature of n-dimensional space curves in grey-value images , 2002, IEEE Trans. Image Process..

[15]  Jean-Bernard Martens,et al.  Subjective quality assessment of compressed images , 1997, Signal Process..

[16]  Maria Petrou,et al.  Irregularly sampled scenes , 2004, SPIE Remote Sensing.

[17]  Touradj Ebrahimi,et al.  Object-based video: extraction tools, evaluation metrics, and applications , 2003, Visual Communications and Image Processing.

[18]  R. Piroddi.  Multiple-feature object-based segmentation of video sequences , 2004 .

[19]  Touradj Ebrahimi,et al.  Objective evaluation of segmentation quality using spatio-temporal context , 2002, Proceedings. International Conference on Image Processing.

[20]  Carl-Fredrik Westin,et al.  Normalized and differential convolution , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Brian A. Wandell,et al.  Color image fidelity metrics evaluated using image distortion maps , 1998, Signal Process..

[22]  Y. J. Zhang,et al.  A survey on evaluation methods for image segmentation , 1996, Pattern Recognit..

[23]  A. Murat Tekalp,et al.  Metrics for performance evaluation of video object segmentation and tracking without ground-truth , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[24]  Ferran Marqués,et al.  Region-based representations of image and video: segmentation tools for multimedia services , 1999, IEEE Trans. Circuits Syst. Video Technol..

[25]  Lucas J. van Vliet,et al.  Normalized Averaging Using Adaptive Applicability Functions with Applications in Image Reconstruction from Sparsely and Randomly Sampled Data , 2003, SCIA.

[26]  최재각,et al.  결합 유사성 척도를 이용한 시공간 영상 분할 ( Spatio-Temporal Video Segmentation Using a Joint Similarity Measure ) , 1997 .

[27]  Guojun Lu,et al.  Segmentation of moving objects in image sequence: A review , 2001 .

[28]  Wijnand A. IJsselsteijn,et al.  A survey of perceptual quality issues in three-dimensional television systems , 2003, IS&T/SPIE Electronic Imaging.

[29]  Roberta Piroddi,et al.  Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach , 2002, BMVC.

[30]  A. Murat Tekalp,et al.  Performance measures for video object segmentation and tracking , 2003, IEEE Transactions on Image Processing.

[31]  Fernando Pereira,et al.  Objective evaluation of relative segmentation quality , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).