基于图像空间相关性与纹理的HEVC块划分快速算法

doi:10.11959/j.issn.1000-0801.2015006

摘要/Abstract

摘要：

为降低高效率视频编码（HEVC）帧内编码算法复杂度，提出一种基于视频内容空间相关性和纹理复杂度的块划分快速算法。首先，通过分析视频测试序列相邻编码树单元（CTU）之间的划分深度范围的相关性，提出CTU最有可能深度范围（MPDR）的概念及计算方法。其次，通过检测相邻CTU之间的边缘方向分析邻近CTU间的纹理差异，并决定当前待编码CTU的划分深度范围是采用MPDR，还是采用标准规定的0～3。最后，通过统计分析得到纹理复杂的阈值计算式，在每个编码单元（CU）编码之前，通过比较像素方差与阈值来分析该CU的纹理复杂程度，并判断是否可以跳过该深度块的编码计算而直接划分。实验结果表明，与HM13.0原有算法相比，本文算法以编码比特率增加0.84%、峰值信噪比下降0.04 dB为代价，能够将编码时间减少20%左右。

关键词: HEVC, 快速块划分, 空间相关性, 最有可能深度范围, 纹理差异, 纹理复杂度

Abstract:

In order to reduce the intra coding complexity of HEVC, a fast block partitioning algorithm based on spatial correlation and image texture was proposed. Firstly, a concept of most possible depth range（MPDR）was proposed according to the correlation analysis of adjacent CTU depth range. Secondly, the difference between the current CTU texture and neighboring CTU texture was analyzed by detecting the dominant edge direction. According to the texture difference, the depth range of the current CTU was chose from MPDR and the origin range 0～3. Finally, a formula for texture complex threshold was formed by statistical calculation. Before CU coding, detect the texture complexity of the current CU by comparing the pixel variance and threshold, determine whether or not to skip the current unit coding calculation. Compared with HM13.0, experimental results reveal that the proposed algorithm can save about 20% computational complexity on average whereas the average bit rate increment is 0.84% and the decrement of PSNR is 0.04 dB.

Key words: high efficiency video coding, fast block partitioning, spatial correlation, most possible depth range, texture difference, texture complexity

姚英彪,李晓娟. 基于图像空间相关性与纹理的HEVC块划分快速算法[J]. 电信科学, 2015, 31(1): 32-40.

Yingbiao Yao,Xiaojuan Li. Fast Block Partitioning Algorithm for HEVC Based on Spatial Correlation and Image Texture[J]. Telecommunications Science, 2015, 31(1): 32-40.

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参考文献 21

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序列	分辨率	p_mindepth0	p_mindepth1	p_mindepth2	p_mindepth3
Traffic	2 560 ppi×1 600 ppi	4.98%	55.41%	39.15%	0.46%
Kimono1	1 920 ppi×1 080 ppi	25.15%	71.60%	3.248%	0.002%
BasketballDrill	832 ppi×480 ppi	0.87%	45.31%	50.78%	3.04%
BQSquare	416 ppi×240 ppi	0.001%	30.21%	54.79%	15.00%
Vidyo1	1 280 ppi×720 ppi	9.28%	65.64%	25.04%	0.04%

序列	分辨率	p_maxdepth0	p_maxdepth1	p_maxdepth2	p_maxdepth3
Traffic	2 560 ppi×1 600 ppi	4.98%	4.83%	7.32%	82.87%
Kimono1	1 920 ppi×1 080 ppi	25.15%	36.22%	16.13%	22.50%
BasketballDrill	832 ppi×480 ppi	0.87%	1.56%	4.51%	93.06%
BQSquare	416 ppi×240 ppi	0.001%	0.003%	0.009%	99.987%
Vidyo1	1 280 ppi×720 ppi	9.28%	8.22%	15.68%	66.81%

序列	最小划分深度大于或等于任意一个邻近CTU 最小深度的平均概率	最大划分深度小于或等于任意一个邻近CTU 最大深度的平均概率
Traffic	94.76%	99.13%
Kimono1	94.20%	92.88%
BasketballDrill	95.67%	100.00%
BQSquare	90.67%	100.00%
Vidyo1	96.84%	97.22%

视频序列	分辨率		BD-Rate	BD-PSNR/dB		?T
视频序列	分辨率	本文算法	参考文献[10]算法	本文算法参考文献[10]算法	本文算法	参考文献[10]算法
Traffic	2 560 ppi×1 600 ppi	0.95%	2.19%	-0.05-0.1	-17.9%	-22.3%
PeopleOnStreet	2 560 ppi×1 600 ppi	2.01%	2.37%	-0.11-0.12	-22.5%	-21.6%
Kimono1	1 920 ppi×1 080 ppi	1.33%	1.03%	-0.05-0.03	-33.6%	-36.0%
BQTerrace	1 920 ppi×1 080 ppi	1.02%	2.4%	-0.05-0.13	-22.1%	-25.5%
BasketballDrive	1 920 ppi×1 080 ppi	1.07%	3.04%	-0.03-0.06	-17.7%	-31.8%
RaceHorsesC	832 ppi×480 ppi	0.49%	1.44%	-0.03-0.08	-16.9%	-16.9%
PartyScene	832 ppi×480 ppi	0.11%	0.97%	-0.01-0.07	-17.9%	-17.8%
BQMall	832 ppi×480 ppi	1.03%	2.06%	-0.06-0.12	-17.9%	-18.6%
BQSquare	416 ppi×240 ppi	0.55%	1.03%	-0.05-0.08	-31.2%	-14.9%
BlowingBubbles	416 ppi×240 ppi	0.07%	1.18%	0.00-0.06	-16.1%	-15.1%
RaceHorse	416 ppi×240 ppi	0.35%	1.05%	-0.02-0.07	-18.3%	-12.9%
Average		0.84%	1.77%	-0.04-0.08	-20.1%	-21.9%

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