title: Bayesian resolution enhancement of compressed video.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA. asegall@ece.nwu.edu
authors.forename: C Andrew
authors.initials: CA
authors.lastname: Segall
authors.forename: Aggelos K
authors.initials: AK
authors.lastname: Katsaggelos
authors.forename: Rafael
authors.initials: R
authors.lastname: Molina
authors.forename: Javier
authors.initials: J
authors.lastname: Mateos
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*