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基于时域预测模型的灵活视频编码快速交叉 CU 分区。

Temporal Prediction Model-Based Fast Inter CU Partition for Versatile Video Coding.

机构信息

College of Computer Science, University of South China, Hengyang 421001, China.

Faculty of Physics and Electronic Information Science, Hengyang Normal University, Hengyang 421002, China.

出版信息

Sensors (Basel). 2022 Oct 12;22(20):7741. doi: 10.3390/s22207741.

DOI:10.3390/s22207741
PMID:36298092
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9606901/
Abstract

Versatile video coding (VVC) adopts an advanced quad-tree plus multi-type tree (QTMT) coding structure to obtain higher compression efficiency, but it comes at the cost of a considerable increase in coding complexity. To effectively reduce the coding complexity of the QTMT-based coding unit (CU) partition, we propose a fast inter CU partition method based on a temporal prediction model, which includes early termination QTMT partition and early skipping multi-type tree (MT) partition. Firstly, according to the position of the current CU, we extract the optimal CU partition information of the position corresponding to the previously coded frames. We then establish a temporal prediction model based on temporal CU partition information to predict the current CU partition. Finally, to reduce the cumulative of errors of the temporal prediction model, we further extract the motion vector difference (MVD) of the CU to determine whether the QTMT partition can be terminated early. The experimental results show that the proposed method can reduce the inter coding complexity of VVC by 23.19% on average, while the Bjontegaard delta bit rate (BDBR) is only increased by 0.97% on average under the Random Access (RA) configuration.

摘要

灵活视频编码(VVC)采用先进的四叉树加多种类型树(QTMT)编码结构,以获得更高的压缩效率,但这也导致编码复杂度显著增加。为了有效降低基于 QTMT 的编码单元(CU)划分的编码复杂度,我们提出了一种基于时间预测模型的快速交叉 CU 划分方法,包括提前终止 QTMT 划分和提前跳过多种类型树(MT)划分。首先,根据当前 CU 的位置,我们从先前编码帧的对应位置提取最佳 CU 划分信息。然后,我们基于时间 CU 划分信息建立时间预测模型,以预测当前 CU 的划分。最后,为了减少时间预测模型的累积误差,我们进一步提取 CU 的运动矢量差值(MVD),以确定是否可以提前终止 QTMT 划分。实验结果表明,所提出的方法可以将 VVC 的交叉编码复杂度平均降低 23.19%,而在随机访问(RA)配置下,平均 Bjontegaard 差分比特率(BDBR)仅增加 0.97%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/488e28d57ef8/sensors-22-07741-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/dcb1329f7653/sensors-22-07741-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/f2161e9d6ba5/sensors-22-07741-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/b957f1ca49c2/sensors-22-07741-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/473d9c822311/sensors-22-07741-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/488e28d57ef8/sensors-22-07741-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/dcb1329f7653/sensors-22-07741-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/f2161e9d6ba5/sensors-22-07741-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/b957f1ca49c2/sensors-22-07741-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/473d9c822311/sensors-22-07741-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fd1/9606901/488e28d57ef8/sensors-22-07741-g005.jpg

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本文引用的文献

1
A Fast Decision Algorithm for VVC Intra-Coding Based on Texture Feature and Machine Learning.基于纹理特征和机器学习的 VVC 帧内编码快速决策算法。
Comput Intell Neurosci. 2022 Sep 13;2022:7675749. doi: 10.1155/2022/7675749. eCollection 2022.
2
Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding.低复杂度多变换选择结合多类型树分割算法的通用视频编码。
Sensors (Basel). 2022 Jul 25;22(15):5523. doi: 10.3390/s22155523.
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DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC.
深度QTMT:一种基于深度学习的用于帧内模式VVC的快速基于QTMT的CU划分方法。
IEEE Trans Image Process. 2021;30:5377-5390. doi: 10.1109/TIP.2021.3083447. Epub 2021 Jun 3.
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Tunable VVC Frame Partitioning based on Lightweight Machine Learning.基于轻量级机器学习的可调式可变视频编码(VVC)帧分区
IEEE Trans Image Process. 2019 Sep 6. doi: 10.1109/TIP.2019.2938670.
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Reducing Complexity of HEVC: A Deep Learning Approach.降低高效视频编码(HEVC)的复杂度:一种深度学习方法。
IEEE Trans Image Process. 2018 Jun 13. doi: 10.1109/TIP.2018.2847035.
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