Super-resolution reconstruction of compressed video using transform-domain statistics

• Published in: IEEE transactions on image processing Vol. 13; no. 1; pp. 33 - 43
• Main Authors: Gunturk, B.K., Altunbasak, Y., Mersereau, R.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-01-2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive noise; Algorithms; Applied sciences; Artificial intelligence; Compressed; Compressing; Computer science; control theory; systems; Computer Simulation; Data Compression - methods; Errors; Exact sciences and technology; HDTV; Image coding; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image reconstruction; Image resolution; Information Storage and Retrieval - methods; Information, signal and communications theory; Models, Statistical; Pattern Recognition, Automated; Pattern recognition. Digital image processing. Computational geometry; Quantization; Reconstruction; Reproducibility of Results; Sensitivity and Specificity; Signal processing; Signal Processing, Computer-Assisted; Signal resolution; Spatial resolution; Statistics; Subtraction Technique; Telecommunications and information theory; Video compression; Video Recording - methods; Additive noise; Quantization error; Image processing; Superresolution; super-resolution; Video signal; Image compression; POCS; Stochastic method; Image reconstruction; DCT-domain reconstruction; multiframe image reconstruction; A posteriori estimation; Discrete cosine transforms; MAP
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2003.819221

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.