Fast Multi-Type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network
- 주제(키워드) Encoding , Complexity theory , Image coding , Transforms , Tools , Standards , Quantization (signal) , Block partitioning , Deep learning , Encoding complexity , Image compression , Intra prediction , Multi-type tree , Neural network , Video compression , VVC
- 주제(기타) Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications
- 설명문(일반) [Park, Sang-hyo] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea; [Kang, Je-Won] Ewha Womans Univ, Dept Elect & Elect Engn, Seoul, South Korea; [Kang, Je-Won] Ewha Womans Univ, Smart Factory Multidisciplinary Program, Seoul, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000183849
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/TMM.2020.3042062
초록/요약
In this paper, we propose a fast decision scheme using a lightweight neural network (LNN) to avoid redundant block partitioning in versatile video coding (VVC). A more versatile block structure, named the multi-type tree (MTT) structure, which includes binary trees (BTs) and ternary trees (TTs), is adopted by VCC, in addition to the traditional quadtree structure. The MTT improved the coding efficiency compared with previous video coding standards. However, the new tree structures, mainly TT, significantly increased the complexity of the VVC encoder. Although widespread application of VVC has been inhibited, this problem has not yet been investigated thoroughly in the literature. In this study, we first determine the statistical characteristics of coded parameters that exhibit correlation with the TT and develop two useful types of features-explicit VVC features (EVFs) and derived VVC features (DVFs)-to facilitate the intra coding of VVC. These features can be obtained efficiently during the intra prediction before the determination of the best block partitioning during rate-distortion optimization in VVC encoding. Our LNN model decides whether to terminate the nested TT block structures subsequent to a quadtree based on the features. The experimental results confirm that the proposed method substantially decreases the encoding complexity of VVC with a slight coding loss under the All Intra configuration. Our code, models, and dataset are available at https://github.com/foriamweak/MTTPartitioning_LNN.
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