Bayesian Resolution Enhancement of Compressed Video
IEEE Transactions on Image ProcessingPublished 7 June 2004
C.A. Segall, Aggelos K. Katsaggelos, Rafael Molina, Javier Mateos
Citations118
SJR quartileQ1
SJR score2.50
SNIP3.41
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TL;DR
This paper considers the impact of video compression on the super-resolution task, and utilizes the Bayesian framework to incorporate information from Hybrid motion-compensation and transform coding schemes to fuse thesuper-resolution and post-processing problems.
Abstract
Super-resolution algorithms recover high-frequency information from a sequence of low-resolution observations. In this paper, we consider the impact of video compression on the super-resolution task. Hybrid motion-compensation and transform coding schemes are the focus, as these methods provide observations of the underlying displacement values as well as a variable noise process. We utilize the Bayesian framework to incorporate this information and fuse the super-resolution and post-processing problems. A tractable solution is defined, and relationships between algorithm parameters and information in the compressed bitstream are established. The association between resolution recovery and compression ratio is also explored. Simulations illustrate the performance of the procedure with both synthetic and nonsynthetic sequences.
Keywords
Computer Science
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