Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/695
Title: Context-aware block-based motion estimation algorithm for multimedia internet of things (IoT) platform
Authors: Saha, Avishek
Lee, Young-Woon
Hwang, Young-Sup
Psannis, Kostas E.
Kim, Byung-Gyu
Type: Article
Subjects: FRASCATI::Engineering and technology
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: internet of things (IoT)
Block-based motion estimation
Motion degree
Adaptive pattern
HEVC
Issue Date: 2018
Publisher: Springer
Source: Personal and Ubiquitous Computing
Volume: 22
Issue: 1
First Page: 163
Last Page: 172
Abstract: Shaping 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 precision
URI: https://doi.org/10.1007/s00779-017-1058-5
https://ruomo.lib.uom.gr/handle/7000/695
ISSN: 1617-4909
1617-4917
Other Identifiers: 10.1007/s00779-017-1058-5
Appears in Collections:Department of Applied Informatics

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