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社区首页 >专栏 >A Fast QTMT Partition Decision Strategy for VVC Intra Prediction

A Fast QTMT Partition Decision Strategy for VVC Intra Prediction

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Aidol
修改2020-08-18 10:01:06
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修改2020-08-18 10:01:06
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文章被收录于专栏:苦旅苦旅苦旅

论文链接https://ieeexplore.ieee.org/document/9110597

摘要

Different from the traditional quaternary tree (QT) structure utilized in the previous generation video coding standard H.265/HEVC, a brand new partition structure named quadtree with nested multitype tree (QTMT) is applied in the latest codec H.266/VVC. The introduction of QTMT brings in superior encoding performance at the cost of great time-consuming. Therefore, a fast intra partition algorithm based on variance and Sobel operator is proposed in this paper. The proposed method settles the novel asymmetrical partition issue in VVC by well balancing the reduction of computational complexity and the loss of encoding quality. To be more concrete, we first terminate further splitting of a coding unit (CU) when the texture of it is judged as smooth. Then, we use Sobel operator to extract gradient features to decide whether to split this CU by QT, thus terminating further MT partitions. Finally, a completely novel method to choose only one partition from five QTMT partitions is applied. Obviously, homogeneous area tends to use a larger CU as a whole to do prediction while CUs with complicated texture are prone to be divided into small sub-CUs and these sub-CUs usually have different textures from each other. We calculate the variance of variance of each sub-CU to decide which partition will distinguish the sub-textures best. Our method is embedded into the latest VVC official reference software VTM-7.0. Comparing to anchor VTM-7.0, our method saves the encoding time by 49.27% on average at the cost of only 1.63% BDBR increase. As a traditional scheme based on variance and gradient to decrease the computational complexity in VVC intra coding, our method outperforms other relative existing state-of-the-art methods, including traditional machine learning and convolution neural network methods.

概述

作者在这篇论文中提出了一种基于方差和Sobel算子的快速帧内分割算法。首先判断一个CU是否平滑,若平滑则停止进一步划分。否则利用Sobel算子提取梯度特征,以决定是否通过QT拆分此CU,从而停止进一步的MT划分。 最后,作者在这篇论文提出了一个新的策略从五个QTMT中选择一个划分模式。

算法的伪代码如下:

图1 算法伪代码
图1 算法伪代码

该算法只适用于Y通道,因此算法第一步就是判断通道,在Cu的尺寸和通道都满足的情况下就开始计算当前Cu的RDO和方差。接着利用Sobel算子计算当前CU的梯度并进行绝对值累加求和得到DX和DY。然后把DX和DY阈值TH1进行比较,若小于TH1,则不进行划分,若大于TH1则进行进一步判断。如果DX和DY满足条件(1<DX/DY< TH2) or (1<DY/DX< TH2)) and ((DX> TH3) and (DY> TH3)则利用QT进行划分从而省去了MT的判断,节约了时间,否则分别按公式计算MT划分中的方差的方差,选择方差最大的模式作为划分的模式。计算方差的方差的公式如下:

图2 计算公式
图2 计算公式

阈值TH1、TH2、TH3的计算方式如下:

图3 计算公式
图3 计算公式

α、β、γ都是参数,作者根据DIV2K数据集进行实验,选取了(α , β , γ ) 的值为(9, 2.7, 30000)。

图4  α , β , γ  选取依据
图4 α , β , γ 选取依据

图1 α , β , γ 选取依据

实验结果如下:

与VTM-7.0相比,该论文提出的算法在BDBR增加1.63%的情况下能节约49.27%的编码时间。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

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