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

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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.

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References

  1. Atzori L, Iera A, Morabito G (2010) The internet of things: A survey. Comput Netw 54:2787–2805

  2. Ericsson (2013) More than 50 billion connected devices. Technical report, Ericsson, Stockholm

    Google Scholar 

  3. Wiegand T, Sullivan GJ, Bjontegaard G, Luthra A (2003) Overview of the h.264/AVC video coding standard. IEEE Trans Circuits Syst Video Technol 13(7):560–576

    Article  Google Scholar 

  4. Bross B, Han WJ, Ohm JR, Sullivan GJ, Wang YK, Wiegand T (2013) High efficiency video coding (HEVC) text specification draft 10. In: Joint collaborative team on video coding (JCTVC) of ITU-T VCEG and ISO/IEC MPEG, Geneva, CH, JCTVC-L1003

  5. Jain JR, Jain AK (1981) Displacement measurement and its application in interframe image coding. IEEE Trans Commun 29(12):1799–1808

    Article  MathSciNet  Google Scholar 

  6. Koga T, Iinuma K, Hirano A, Iijima Y, Ishiguro T (1981) Motion-compensated interframe coding for video conferencing. Proc. Nat. Telecom. Conf. New Orleans, LA G.5.3.1G.5.3.5

  7. Li R, Zeng B, Liou ML (1994) A new three-step search algorithm for block motion estimation. IEEE Trans Circuits Syst Video Technol 4(4):432–442

    Google Scholar 

  8. Po L-M, Ma W-C (1996) A novel four-step search algorithm for fast block motion estimation. IEEE Trans Circuits Syst Video Technol 6(3):313–317

    Article  Google Scholar 

  9. Zhu S, Ma K-K (2000) A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans Image Process 9(2):287–290

    Article  Google Scholar 

  10. Cheung C-H, Po L-M (2002) A novel cross-diamond search algorithm for fast block motion estimation. IEEE Trans Circuits Syst Video Technol 12(12):1168–1177

    Article  Google Scholar 

  11. Lam C-W, Po L-M, Cheung CH (2003) A new cross-diamond search algorithm for fast block matching motion estimation. Proc Intern Conf Neural Netw Signal Process 2:1262–1265

    Google Scholar 

  12. Cheung CH, Po LM (2002) A novel smallcrossdiamond search algorithm for fast video coding and videoconferencing applications. In: Proceedings IEEE international conference on image processing, pp 681–684

  13. Yun C, Dai K, Wang Z, Guo J (2005) A fast motion estimation algorithm based on diamond and simplified square search patterns. In: Iberoamerican congress on pattern recognition, pp 440– 449

    Google Scholar 

  14. Shen-Chuan T, Chen Y-R, Chen Y-H (2007) Small-diamond-based search algorithm for fast block motion estimation. Signal Process Image Commun 22(10):877–890

    Article  Google Scholar 

  15. Djoudi K, Belbachir MF (2015) Star diamond: an efficient algorithm for fast block matching motion estimation in h264/AVC video codec. In: Multimedia tools and applications, pp 1–15

  16. Zhu C, Lin X, Chau L-P (2002) Hexagon-based search pattern for fast block motion estimation. IEEE Trans Circuits Syst Video Technol 12(5):349–355

    Article  Google Scholar 

  17. Ohm J-R, Sullivan GJ, Schwarz H, Tan TK, Wiegand T (2012) Comparison of the coding efficiency of video coding standards—including high efficiency video coding (HEVC). IEEE Trans Circuits Syst Video Technol 22(12):1669–1684

    Article  Google Scholar 

  18. HEVC Test Model (HM), HEVC Reference software [Online]. Available: https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/

  19. Bossen F (2012) Common test conditions and software reference configurations JCTVC-i1100, JCT-VC document 9th Meeting, Geneva

  20. Bjϕntegaard G (2001) Calculation of Average PSNR Differences between RD curves. In: ITU-T SG16/Q6, 13th VCEG Meeting, Austin, Texas, USA, Doc. VCEG-M33

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Computer Engineering, SunMoon University, Asan, Republic of Korea

    Avishek Saha, Young-Woon Lee & Young-Sup Hwang

  2. Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

    Kostas E. Psannis

  3. Department of IT Engineering, Sookmyung Women’s University, Seoul, Republic of Korea

    Byung-Gyu Kim

Authors
  1. Avishek Saha
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  2. Young-Woon Lee
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  3. Young-Sup Hwang
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  4. Kostas E. Psannis
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  5. Byung-Gyu Kim
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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). https://doi.org/10.1007/s00779-017-1058-5

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  • DOI: https://doi.org/10.1007/s00779-017-1058-5

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