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Context-aware block-based motion estimation algorithm for multimedia internet of things (IoT) platform


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.

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This research was supported by the Sookmyung Women’s University Research Grants (1-1703-2008).

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Correspondence to Byung-Gyu Kim.

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Saha, A., Lee, YW., Hwang, YS. et al. Context-aware block-based motion estimation algorithm for multimedia internet of things (IoT) platform. Pers Ubiquit Comput 22, 163–172 (2018).

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  • IoT
  • Block-based motion estimation
  • Motion degree
  • Adaptive pattern
  • HEVC