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JBE, vol. 26, no. 5, pp.652-655, September, 2021

DOI: https://doi.org/10.5909/JBE.2021.26.5.652

Motion Vector Resolution Decision Algorithm based on Neural Network for Fast VVC Encoding

Han-gyul Baek and Sang-hyo Park

C.A E-mail: s.park@knu.ac.kr

Abstract:

Among various inter prediction techniques of Versatile Video Coding (VVC), adaptive motion vector resolution (AMVR) technology has been adopted. However, for AMVR, various MVs should be tested per each coding unit, which needs a computation of rate-distortion cost and results in an increase in encoding complexity. Therefore, in order to reduce the encoding complexity of AMVR, it is necessary to effectively find an optimal AMVR mode. In this paper, we propose a lightweight neural network-based AMVR decision algorithm based on more diverse datasets. 



Keyword: VVC, inter prediction, motion vector resolution, encoding complexity, Multi-layer perceptron

Reference:
[1] B. Bross, J. Chen, S. Liu, Y. -K. Wang, “Versatile Video Coding (Draft 7),” Joint Video Experts Team (JVET) of ITU-T and ISO/IEC, Document JVET-P2001, 2019.
[2] High Efficiency Video Coding (HEVC), Rec. ITU-T H.265 and ISO/IEC 23008-2, ITU-T and ISO/IEC JTC 1, 2013 (and subsequent editions).
[3] H. Han, J. Choe, D. Gwon, and H. Choi, “VVC intra prediction and encoding key technology”, Broadcasting and Media Magazine, Vol.24, No.4, pp.39-59, October 2019.
[4] S. -h. Park, "Fast Decision Method of Adaptive Motion Vector Resolution", The Korean Institute of Broadcast and Media Engineers, Vol.25, No.3, pp.305-312, May 2020, http://dx.doi.org/10.5909/JBE.2020.25.3.305
[5] H. Baek and S. -h. Park, "Neural Network-Based Adaptive Motion Vector Resolution Discrimination Technique", Proceedings of the Korean Society of Broadcast Engineers Conference, pp. 49-51, 2021, in press
[6] S. -h. Park and J. Kang, "Fast Multi-type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network, " in IEEE Transactions on Multimedia, pp.1-1, Dec 2020, doi:10.1109/TMM.2020.3042062.
[7] Ultra video group (UVG) dataset, http://ultravideo.cs.tut.fi/#testsequences (Accessed Oct. 30, 2020).
[8] F. Bossen, J. Boyce, K. Suehring, X. li, and V. Seregin, “JVET common test conditions and software reference configurations for SDR video,” Joint Video Experts Team (JVET) of ITU-T and ISO/IEC, Document JVET-N1010-v1, 2019.

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