Detecting Cross-Fades in Interlaced Video With 3:2 Film Cadence

This letter presents an algorithm for detecting cross-fade scene changes in video carrying 3:2 or mixed film cadence. There are many existing methods for detecting gradual video transitions, but none of the current literature addresses the complication of film cadence. The differences between video and film capture and the mechanics of the telecine transfer process used to convert 24-Hz film to the main international television standards alter the temporal properties in a nonlinear way that makes cross-fades more difficult to detect with existing methods. An algorithm is proposed to address this problem.

[1]  S. Lefèvre,et al.  REAL TIME TEMPORAL SEGMENTATION OF COMPRESSED AND UNCOMPRESSED DYNAMIC COLOUR IMAGE SEQUENCES , 2000 .

[2]  Hyeokman Kim,et al.  Detection of gradual scene changes for parsing of video data , 1997, Electronic Imaging.

[3]  Ramin Zabih,et al.  A feature-based algorithm for detecting and classifying scene breaks , 1995, MULTIMEDIA '95.

[4]  Shih-Fu Chang,et al.  Scene change detection in an MPEG-compressed video sequence , 1995, Electronic Imaging.

[5]  Ullas Gargi,et al.  Performance characterization of video-shot-change detection methods , 2000, IEEE Trans. Circuits Syst. Video Technol..

[6]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[7]  Paul Over,et al.  TRECVID 2006 Overview , 2006, TRECVID.

[8]  John S. Boreczky,et al.  Comparison of video shot boundary detection techniques , 1996, Electronic Imaging.

[9]  Rainer Lienhart,et al.  Comparison of automatic shot boundary detection algorithms , 1998, Electronic Imaging.

[10]  Ba Tu Truong,et al.  New enhancements to cut, fade, and dissolve detection processes in video segmentation , 2000, ACM Multimedia.

[11]  Wei Xiong,et al.  Efficient Scene Change Detection and Camera Motion Annotation for Video Classification , 1998, Comput. Vis. Image Underst..

[12]  Alan F. Smeaton,et al.  Evaluation of automatic shot boundary detection on a large video test suite , 1999 .

[13]  Shengrui Wang,et al.  Motion Insensitive Detection of Cuts and Gradual Transitions in Digital Videos , 2002 .

[14]  Behzad Shahraray,et al.  Scene change detection and content-based sampling of video sequences , 1995, Electronic Imaging.

[15]  Warnakulasuriya Anil Chandana Fernando,et al.  Fade and dissolve detection in uncompressed and compressed video sequences , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[16]  R. Brunelli,et al.  A Survey on the Automatic Indexing of Video Data, , 1999, J. Vis. Commun. Image Represent..

[17]  Nicole Vincent,et al.  A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval , 2003, Real Time Imaging.

[18]  Yukinobu Taniguchi,et al.  PanoramaExcerpts: extracting and packing panoramas for video browsing , 1997, MULTIMEDIA '97.

[19]  Thomas D. C. Little,et al.  A Survey of Technologies for Parsing and Indexing Digital Video1 , 1996, J. Vis. Commun. Image Represent..

[20]  Bruce Devlin,et al.  The MXF Book: An Introduction to the Material eXchange Format , 2006 .

[21]  Paul England,et al.  Comparison of automatic video segmentation algorithms , 1996, Other Conferences.

[22]  Atreyi Kankanhalli,et al.  Automatic partitioning of full-motion video , 1993, Multimedia Systems.

[23]  Irena Koprinska,et al.  Temporal video segmentation: A survey , 2001, Signal Process. Image Commun..

[24]  Michael Robin,et al.  Digital Television Fundamentals: Design and Installation of Video and Audio Systems , 1997 .

[25]  Dragutin Petkovic,et al.  Content-based representation and retrieval of visual media: A state-of-the-art review , 1996, Multimedia Tools and Applications.