Bayesian resolution enhancement of compressed video

C. Andrew Segall*, Aggelos K. Katsaggelos, Rafael Molina, Javier Mateos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)898-910
Number of pages13
JournalIEEE Transactions on Image Processing
Volume13
Issue number7
DOIs
StatePublished - Jul 2004

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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