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Statistical Conditional Sampling for Variable-Resolution Video Compression

Author

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  • Alexander Wong
  • Mohammad Javad Shafiee
  • Zohreh Azimifar

Abstract

In this study, we investigate a variable-resolution approach to video compression based on Conditional Random Field and statistical conditional sampling in order to further improve compression rate while maintaining high-quality video. In the proposed approach, representative key-frames within a video shot are identified and stored at full resolution. The remaining frames within the video shot are stored and compressed at a reduced resolution. At the decompression stage, a region-based dictionary is constructed from the key-frames and used to restore the reduced resolution frames to the original resolution via statistical conditional sampling. The sampling approach is based on the conditional probability of the CRF modeling by use of the constructed dictionary. Experimental results show that the proposed variable-resolution approach via statistical conditional sampling has potential for improving compression rates when compared to compressing the video at full resolution, while achieving higher video quality when compared to compressing the video at reduced resolution.

Suggested Citation

  • Alexander Wong & Mohammad Javad Shafiee & Zohreh Azimifar, 2012. "Statistical Conditional Sampling for Variable-Resolution Video Compression," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-7, October.
    • Handle: RePEc:plo:pone00:0045002
    DOI: 10.1371/journal.pone.0045002
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