title: Super-resolution reconstruction of compressed video using transform-domain statistics.
abstract: Considerable attention has been directed to the problem of producing high-resolution video and still images from multiple low-resolution images. This multiframe reconstruction, also known as super-resolution reconstruction, is beginning to be applied to compressed video. Super-resolution techniques that have been designed for raw (i.e., uncompressed) video may not be effective when applied to compressed video because they do not incorporate the compression process into their models. The compression process introduces quantization error, which is the dominant source of error in some cases. In this paper, we propose a stochastic framework where quantization information as well as other statistical information about additive noise and image prior can be utilized effectively.
authors.affiliation: Louisiana State University, Baton Rouge, LA 70803 USA. bahadir@ece.lsu.edu
authors.forename: Bahadir K
authors.initials: BK
authors.lastname: Gunturk
authors.forename: Yucel
authors.initials: Y
authors.lastname: Altunbasak
authors.forename: Russell M
authors.initials: RM
authors.lastname: Mersereau
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*