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On Compression of Encrypted Video

Authors: Daniel Schonberg; Chuohao Yeo; Stark C. Draper; Kannan Ramchandran;

On Compression of Encrypted Video

Abstract

We consider video sequences that have been encrypted uncompressed. Since encryption masks the source, traditional data compression algorithms are rendered ineffective. However, it has been shown that through the use of distributed source-coding techniques, the compression of encrypted data is in fact possible. This means that it is possible to reduce data size without requiring that the data be compressed prior to encryption. Indeed, under some reasonable conditions, neither security nor compression efficiency need be sacrificed when compression is performed on the encrypted data (Johnson et al., 2004). In this paper we develop an algorithm for the practical lossless compression of encrypted gray scale video. Our method is based on considering the temporal correlations in the video. This move to temporal dependence builds on our previous work on memoryless sources, and one- and two-dimensional Markov sources. For comparison, a motion-compensated lossless video encoder can compress each unencrypted frame of the standard "Foreman" test video sequence by about 57%. Our algorithm can compress the same frames, after encryption, by about 33%

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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