Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/695
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSaha, Avishek-
dc.contributor.authorLee, Young-Woon-
dc.contributor.authorHwang, Young-Sup-
dc.contributor.authorPsannis, Kostas E.-
dc.contributor.authorKim, Byung-Gyu-
dc.date.accessioned2020-04-16T07:23:55Z-
dc.date.available2020-04-16T07:23:55Z-
dc.date.issued2018-
dc.identifier10.1007/s00779-017-1058-5en_US
dc.identifier.issn1617-4909en_US
dc.identifier.issn1617-4917en_US
dc.identifier.urihttps://doi.org/10.1007/s00779-017-1058-5en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/695-
dc.description.abstractShaping video data into fast-responding transmission and high resolution output video using cost-effective video processing is desirable in many applications including Internet of Things (IoT) applications. In association with rapid development of IoT smart sensor applications, real-time processing of huge-amount of data for a video signal has become necessary leading to video compression technology. Motion estimation (ME) is necessary for improving the quality, but it has high computational complexity in video compression system. The present article, therefore, proposes a context-aware adaptive pattern-based ME algorithm for multimedia IoT platform to improve video compression. In the proposed algorithm, the motions are classified into large or small based on distortion value. Accordingly, the search pattern is chosen either small diamond search pattern (SDSP) or large diamond search pattern (LDSP) in each and every step of ME; allowing adaptive processing of large and small abstract information. Compared to conventional fast algorithms, the experimental results demonstrate up to 40 and 36% reduction in encoding time for low-delay main (LB-main) and random access main (RA-main) profiles respectively in HEVC test model 16.10 encoder with bit-rate loss of 0.071 and 0.246% for both the profiles, ensuring quality video and searching precisionen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.sourcePersonal and Ubiquitous Computingen_US
dc.subjectFRASCATI::Engineering and technologyen_US
dc.subjectFRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineeringen_US
dc.subject.otherinternet of things (IoT)en_US
dc.subject.otherBlock-based motion estimationen_US
dc.subject.otherMotion degreeen_US
dc.subject.otherAdaptive patternen_US
dc.subject.otherHEVCen_US
dc.titleContext-aware block-based motion estimation algorithm for multimedia internet of things (IoT) platformen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume22en_US
local.identifier.issue1en_US
local.identifier.firstpage163en_US
local.identifier.lastpage172en_US
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
Context-aware block-based motion estimation algorithmfor multimedia internet of things (IoT) platform.pdfContext-aware block-based motion estimation algorithmfor multimedia internet of things (IoT) platform kpsannis .pdf906,91 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.