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dc.contributor.authorZhang, Zhengdong
dc.contributor.authorSze, Vivienne
dc.date.accessioned2021-11-02T14:43:47Z
dc.date.available2021-11-02T14:43:47Z
dc.date.issued2017-07
dc.identifier.urihttps://hdl.handle.net/1721.1/137100
dc.description.abstract© 2017 IEEE. State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e.g., 60Mpixels/s for HD video). This paper introduces FAST (Free Adaptive Super-resolution via Transfer), a framework to accelerate any SR algorithm applied to compressed videos. FAST exploits the temporal correlation between adjacent frames such that SR is only applied to a subset of frames; SR pixels are then transferred to the other frames. The transferring process has negligible computation cost as it uses information already embedded in the compressed video (e.g., motion vectors and residual). Adaptive processing is used to retain accuracy when the temporal correlation is not present (e.g., occlusions). FAST accelerates state-of-the-art SR algorithms by up to 15× with a visual quality loss of 0.2dB. FAST is an important step towards real-time SR algorithms for ultra- HD displays and energy constrained devices (e.g., phones and tablets).en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/cvprw.2017.138en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleFAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videosen_US
dc.typeArticleen_US
dc.identifier.citationZhang, Zhengdong and Sze, Vivienne. 2017. "FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-07-03T16:17:16Z
dspace.date.submission2019-07-03T16:17:17Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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