Real-Time Adaptive Video Compression
Abstract
Compressive sensing has been widely applied to problems in signal and imaging processing. In this work, we present an algorithm for predicting optimal real-time compression rates for video. The video data we consider is spatially compressed during the acquisition process, unlike in many of the standard methods. Rather than temporally compressing the frames at a fixed rate, our algorithm adaptively predicts the compression rate given the behavior of a few previous compressed frames. The algorithm uses polynomial fitting and simple filters, making it computationally feasible and easy to implement in hardware. Based on numerical simulations of real videos, the algorithm is able to capture object motion and approximate dynamics within the compressed frames. The adaptive video compression improves the quality of the reconstructed video (as compared to an equivalent fixed rate compression scheme) by several dB of peak signal-to-noise ratio without increasing the amount of information stored, as seen in numerical simulations presented here.
Additional Information
© 2015 Society for Industrial and Applied Mathematics. Submitted to the journal's Computational Methods in Science and Engineering section September 20, 2013; accepted for publication (in revised form) February 27, 2015; published electronically December 22, 2015. Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA 91125 (hschaeffer @ucla.edu). The research of this author was supported by NSF grant 1303892, by the University of California President's Postdoctoral Fellowship Program, and by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG). Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095 (gyyf11@gmail.com). The research of this author was supported by NSF DMS 0835863, by NSF DMS 0914561, and by ONR N00014-11-0749. Department of Mathematics, University of California at Irvine, Irvine, CA 92697-3875 (zhao@math.uci.edu). The research of this author was supported by ONR grant N00014-11-1-0602 and by NSF DMS-1418422. The authors would like to thank Wotao Yin and Tom Goldstein for their useful discussions. The authors would also like to thank Lawrence Carin, Guillermo Sapiro, David Brady, Giang Tran, Jianbo Yang, Xin Yuan, and the anonymous reviewers for their helpful discussions and comments.Attached Files
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Additional details
- Eprint ID
- 63717
- Resolver ID
- CaltechAUTHORS:20160115-125143091
- 1303892
- NSF
- University of California
- Department of Energy (DOE)
- DMS 0835863
- NSF
- DMS 0914561
- NSF
- N00014-11-0749
- Office of Naval Research (ONR)
- N00014-11-1-0602
- Office of Naval Research (ONR)
- DMS-1418422
- NSF
- National Defense Science and Engineering Graduate (NDSEG) Fellowship
- Created
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2016-01-15Created from EPrint's datestamp field
- Updated
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2023-03-02Created from EPrint's last_modified field