Abstract
Defect detection and restoration of degraded videos is an important topic in media content management systems. Frame pixel-shift is a common form of severe defect in videos caused by loss of consecutive pixels by the video transmission system. Pixel-shift refers to the large amount of pixel shifts one by one due to a small quantity of image data loss. The damaged region in the affected frame is usually quite large, causing serious degradation of visual quality. This paper addresses the issue of how to automatically detect and restore frame pixel-shift in videos. Pixel-shift frame detection relies on spatio-temporal information and motion estimation. Accurate measurement of pixels shift is achieved based on the analysis of temporal frequency information and restoration is accomplished by reversing the pixels shift and spatio-temporal interpolation. Performance evaluation using real video sequences demonstrate the good performance of our algorithm.
Similar content being viewed by others
References
Bernard C (1999) Discrete Wavelet Analysis for Fast Optic Flow Computation, Rapport Interne du Centre de Mathématiques Appliquées RI415, École Polytechnique
Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image inpainting. In Proceedings of ACM SIGGRAPH 2000, 417–424. doi:10.1145/344779.344972
Bruni V, Vitulano D (2004) A generalized model for scratch detection. IEEE Trans Image Process 13(1):44–50. doi:10.1109/TIP.2003.817231
Corrigan D, Kokaram AC (2004) Diagnosis and treatment of film tear in degraded archived media. Proceedings of the 17th International Conference on Pattern Recognition 4:779–782. doi:10.1109/ICPR.2004.1333888
Corrigan D, Kokaram AC (2004) Automated treatment of film tear in degraded archived media. 2004 IEEE International Conference on Image Processing 3:1823–1826. doi:10.1109/ICIP.2004.1421430
Corrigan D, Harte N, Kokaram AC (2007) Automated segmentation of torn frames using the graph cuts technique. 2007 IEEE International Conference on Image Processing, I-557–I-560. doi:10.1109/ICIP.2007.4379015
Fischler MA, Bolles RC (1981) Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun ACM 24:381–395. doi:10.1145/358669.358692
Harris CG, Stephens MJ (1988) A combined corner and edge detector. Proceedings of Fourth Alvey Vision Conference, 147–151
Kokaram AC (2004) On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach. IEEE Trans Image Process 13(3):397–415. doi:10.1109/TIP.2004.823815
Kokaram AC, Godsill SJ (2002) MCMC for joint noise reduction and missing data treatment in degraded video. IEEE Trans Signal Process 50(2):189–205. doi:10.1109/78.978375
Kokaram AC, Morris RD, Fitzgerald WJ, Rayner PJW (1995) Interpolation of missing data in image sequences. IEEE Trans Image Process 11(4):1509–1519. doi:10.1109/83.469932
Komatsu T, Ohuchi T, Saito T (1999) Detection and restoration of film blotches using global motion segmentation. 1999 IEEE International Conference on Image Processing 3:479–483. doi:10.1109/ICIP.1999.817160
Matsushita Y, Ofek E, Tang X, Shum H-Y (2005) Full-frame video stabilization. 2005 IEEE International Conference on Computer Vision and Pattern Recognition 1:50–57. doi:10.1109/CVPR.2005.166
Oppenheim AV, Lim JS (1981) The importance of phase in signals. Proc IEEE 69(5):529–541. doi:10.1109/PROC.1981.12022
Peter K (2003) Phase Congruency Detects Corners and Edges. The Australian Pattern Recognition Society Conference on Digital Image Computing: Techniques and Applications. 309–318
Schallauer P, Morzinger R (2006) Rapid and reliable detection of film grain noise. 2006 IEEE International Conference on Image Processing, 413–416. doi:10.1109/ICIP.2006.312481
Shih TK, Lin LH, Lee WJ (2006) Detection and removal of long scratch lines in aged films. 2006 IEEE International Conference on Multimedia and Expo, 477–480. doi:10.1109/ICME.2006.262576
Tenze L, Carrato S, Ramponi G (2002) An alignment algorithm for old motion pictures. IEEE Signal Process Lett 9(10):309–311. doi:10.1109/LSP.2002.803410
Vlachos T (2004) Flicker correction for archived film sequences using a nonlinear model. IEEE Trans Circ Syst Video Tech 14(4):508–516. doi:10.1109/TCSVT.2004.825559
Acknowledgment
The authors would like to thank the reviewers for their thorough comments and suggestions that helped to improve this paper. The work reported in this paper is supported by the National Natural Science Foundation of China under Grant No.60833009, the National High Technology Research and Development Program of China under Grant No.2009AA01Z305, the Cosponsored Project of Beijing Committee of Education under Grant No.SYS100130422 and the 111 Project under Grant No.B08004.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yuan, H., Ma, H. & Huang, X. Automatic detection and restoration of frame pixel-shift in videos. Multimed Tools Appl 47, 307–323 (2010). https://doi.org/10.1007/s11042-009-0324-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-009-0324-6