Visual complexity measure for playing videos adaptively

Details Visual complexity measure for playing videos adaptively Visual complexity measure for playing videos adaptively

2008-07-29

2008-07-29

Wong, Allen (Department: 2621)

Pulse or digital communications

Bandwidth reduction or expansion

Television or motion video signal

C375S240000, C375S240010, C375S240120

Reexamination Certificate

active

10616546

ABSTRACT:
A method plays frames of a video adaptively according to a visual complexity of the video. First a spatial frequency of pixel within frames of the video is measured, as well as a temporal velocity of corresponding pixels between frames of the video. The spatial frequency is multiplied by the temporal velocity to obtain a measure of the visual complexity of the frames of the video. The frames of the video are then played at a speed that corresponds to the visual complexity.

REFERENCES:
patent: 5528533 (1996-06-01), Kitaura et al.
patent: 5596418 (1997-01-01), Strolle et al.
patent: 5596685 (1997-01-01), Ashton
patent: 6181742 (2001-01-01), Rajagopalan et al.
patent: 2003/0026340 (2003-02-01), Divakaran et al.
Meng et al., “Scene Change Detection in a MPEG Compressed Video Sequence”, Proceedings of the SPIE, vol. 2419, p. 14-25, 1995.
Meng et al., “Scene Change Detection in a MPEG Compressed Video Sequence,” Proceedings of the SPIE, vol. 2419, p. 14-25, 1995.
Peker, “Video Indexing and Summerization Using Motion Activity,” dissertation, New Jersey Institute of Technology, Jan. 2001.
Peker et al., “An Extended Framework for Adaptive Playback-Based Video Summerization,” SPIE Internet Multimedia Management Systems IV, vol. 5242, p. 26-33, 2003.
Zhang et al., “Video Parsing Using Compressed Data,” Proceedings of the SPIE, vol. 2182, p. 142-149, 1994.
S. Pfeiffer et al. in “Abstracting Digital Movies Automactically,” J. Visual Comm. Image Representation, vol. 7, No. 4, pp. 345-353, Dec. 1996.
Hanjalic et al. in “An Integrated Scheme for Automated Video Abstraction Based on Unsupervised Cluster-Validity Analysis,” IEEE Trans. On Circuits and Systems for Video Technology, vol. 9, No. 8, Dec. 1999.
Yeung et al., “Efficient matching and clustering of video shots,” ICIP '95, pp. 338-341, 1995.
Zhong et al., “Clustering methods for video browsing and annotation,” SPIE Storage and Retrieval for Image and Video Databases IV, pp. 239-246, 1996.
Ferman et al., “Efficient filtering and clustering methods for temporal video segmentation and visual summarization,” J. Vis. Commun. & Image Rep., 9:336-351, 1998.
DeMenthon et al., “Video Summarization by Curve Simplification,” ACM Multimedia 98, pp. 211-218, Sep. 1998.
Divakaran et al., “Motion Activity based extraction of key frames from video shots,” Proc. IEEE Int'l Conf. on Image Processing, Sep. 2002.
Ma et al., “A User Attention Model for Video Summarization,” ACM Multimedia '02, pp. 533-542, Dec. 2002.
Divakaran et al., “Video summarization using descriptors of motion activity,” Journal of Electronic Imaging, vol. 10, No. 4, Oct. 2001.
Peker et al., “Constant pace skimming and temporal sub-sampling of video using motion activity,” Proc. IEEE Int'l Conf. on Image Processing, Oct. 2001.
Watson et al., “Window of Visibility: a psychophysical theory of fidelity in time-sampled visual motion displays,” J. Opt. Soc. Am. A, vol. 3, No. 3, pp. 300-307, Mar. 1986.
Van Roosmalen et al. “Embedded Coring in MPEG Video Compression,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, No. 3, Mar. 2002.

Affiliated with

Also associated with

Rating

Visual complexity measure for playing videos adaptively does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Visual complexity measure for playing videos adaptively, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Visual complexity measure for playing videos adaptively will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3928683