Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Motion Vector Resolution Decision Algorithm based on Neural Network for Fast VVC Encoding

고속 VVC 부호화를 위한 신경망 기반 움직임 벡터 해상도 결정 알고리즘

  • Baek, Han-gyul (School of Computer Science and Engineering, Kyungpook National University) ;
  • Park, Sang-hyo (School of Computer Science and Engineering, Kyungpook National University)
  • Received : 2021.08.24
  • Accepted : 2021.09.24
  • Published : 2021.09.30
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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.

Versatile Video Coding(VVC)의 압축 효율을 끌어올리기 위하여 다양한 화면 간 예측(inter prediction)기법 중 적응적 움직임 벡터 해상도(Adaptive motion vector resolution, 이하 AMVR)기술이 채택되어 왔다. 다만, AMVR을 적용하여 최적의 해상도를 결정하기 위해서는 매 부호화 유닛마다 다양한 테스트를 진행해야 하며, 이는 율-왜곡 비용의 계산 복잡도 증가를 야기한다. 따라서 VVC의 부호화 복잡도의 감소를 위해 효과적으로 최적의 AMVR 모드를 찾아야 한다. 본 논문에서는 보다 다양한 데이터셋 기반 하에 경량화된 신경망 기반의 AMVR 결정 알고리즘을 제안한다.

Keywords

Acknowledgement

This study was supported in part by the BK21 FOUR project (AI-driven Convergence Software Education Research Program) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (4199990214394) and was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(No. 2020R1I1A3072227)).

References

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