DocumentCode :
595375
Title :
Bayesian image enlargement for mixed-resolution video
Author :
Jing Tian ; Li Chen
Author_Institution :
Sch. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
3082
Lastpage :
3085
Abstract :
Many scalable video compression techniques utilize a mixed-resolution scheme, which down-samples some frames at the encoder to be reduced-resolution frames while keeping resolutions of other frames unchanged as full resolutions, in order to achieve higher compression gain. Image enlargement technique is required at the decoder to recover the original full-resolution frames for this mixed-resolution video system setup. This paper proposes a Bayesian approach to enlarge the reduced-resolution frame via its maximum a posterior estimation, using the information from the observed reduced-resolution frame, plus more detailed information extracted from available neighboring frames in full resolution. Experiments are conducted to demonstrate the superior performance of the proposed approach.
Keywords :
data compression; image resolution; maximum likelihood estimation; video coding; Bayesian image enlargement; compression gain; full-resolution frames; maximum a posterior estimation; mixed-resolution video system setup; reduced-resolution frames; scalable video compression techniques; Bayesian methods; Image reconstruction; Image resolution; Interpolation; PSNR; Signal resolution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460816
Link To Document :
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