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dc.contributor.authorMi, Y
dc.contributor.authorLuo, C
dc.contributor.authorMin, G
dc.contributor.authorMiao, W
dc.contributor.authorWu, L
dc.contributor.authorZhao, T
dc.date.accessioned2019-03-13T12:00:44Z
dc.date.issued2019-05-10
dc.description.abstractIn this paper, we propose a novel video coding scheme to significantly reduce the coding complexity and enhance overall coding efficiency in videos acquired by high mobility devices such as unmanned aerial vehicles (UAVs). In order to reduce the encoded data bits and encoding time to facilitate real-time data transmission, as well as minimize the image distortion caused by the jitter of onboard camera, a sensor-assisted global motion estimation (GMV) algorithm is designed to calculate perspective transformation model and global motion vectors, which are used in both the inter-frame coding to improve the coding efficiency and intra-frame coding to reduce block search complexity. We conducted comprehensive simulation experiments on official HM-16.10 codec and the performance results show the proposed method can achieve faster block search by 50% to 60% speedup and lower bitrate by 15% to 30% compared with standard HEVC coding software.en_GB
dc.identifier.citation2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019), 12-17 May 2019, Brighton, UK, pp. 2237-2241.en_GB
dc.identifier.doi10.1109/ICASSP.2019.8682798
dc.identifier.urihttp://hdl.handle.net/10871/36436
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rights© 2019 IEEE.
dc.subjectHEVCen_GB
dc.subjectsensor-assisted global motion estimationen_GB
dc.subjectlow delayen_GB
dc.titleSensor-Assisted Global Motion Estimation for Efficient UAV Video Codingen_GB
dc.typeConference paperen_GB
dc.date.available2019-03-13T12:00:44Z
dc.descriptionThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.en_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2019-02-01
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2019-02-01
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2019-03-12T23:36:17Z
refterms.versionFCDAM
refterms.dateFOA2019-05-10T15:27:45Z
refterms.panelBen_GB


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