3 September 2009 Compression of turbulence-affected video signals
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Proceedings Volume 7444, Mathematics for Signal and Information Processing; 744409 (2009); doi: 10.1117/12.827020
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States
Abstract
A video signal obtained through a relatively long-distance atmospheric medium suffers from blur and spatiotemporal image movements caused by the air turbulence. These phenomena, which reduce the visual quality of the signal, reduce also the compression rate for motion-estimation based video compression techniques, and cause an increase of the required bandwidth of the compressed signal. The compression rate reduction results from the frequent large amount of random image local movements which differ from one image to the other, resulting from the turbulence effects. In this research we examined the increase of compression rate by developing and comparing two approaches. In the first approach, a pre-processing image restoration is first performed, which includes reduction of the random movements in the video signal and optionally de-blurring the image. Then, a standard compression process is carried out. In this case, the final de-compressed video signal is a restored version of the recorded one. The second approach intends to predict turbulence-induced motion vectors according to the latest images in the sequence. In this approach the final decompressed image should be as much the same as the recorded image (including the spatiotemporal movements). It was found that the first approach improves the compression ratio. At the second approach it was found that after running short temporal median on the video sequence the turbulence optical flow progress can be predicted very well, but this result was not enough for producing a significant improvement at this stage.
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Shahar Mahpod, Yitzhak Yitzhaky, "Compression of turbulence-affected video signals", Proc. SPIE 7444, Mathematics for Signal and Information Processing, 744409 (3 September 2009); doi: 10.1117/12.827020; https://doi.org/10.1117/12.827020
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KEYWORDS
Image compression

Video compression

Video

Optical flow

Turbulence

Motion estimation

Digital filtering

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