HEVC编码快速算法关键技术研究
发布时间：2018-08-10 20:45
【摘要】：新一代视频编码标准HEVC(High Efficiency Video Coding)的编码效率比H.264/MPEG-4AVC提高了一倍以上。但是对其编码工具的灵活选择使得HEVC编码器复杂度急剧增加,这严重阻碍了HEVC的应用和发展。因此针对HEVC编码快速算法的研究至关重要。 本文在介绍了视频编码技术的概况和视频编码标准的发展历史之后,对HEVC的编码框架进行了概括。然后针对HEVC编码工具的特点,指出了HEVC编码优化的方向：帧内编码单元快速选择算法、帧间编码单元快速选择算法和运动估计快速算法。 针对HEVC帧内编码单元划分复杂度高的问题,提出了一种基于统计学习的帧内编码单元快速选择算法。将帧内编码单元的划分建模为k-means分类问题,通过分析四个子编码单元覆盖区域像素的方差组合而成的四维向量的特征,用简单而有效的k-means分类方法进行编码单元划分的预测,从而避免了基于率失真优化的全搜索算法,降低了编码器的计算复杂度。 针对HEVC帧间编码单元划分复杂度高的问题,提出了基于深度时空相关性的帧间编码单元快速选择算法。在分析了时空相邻编码树单元之间的相关性之后,依据相关性强弱选择最佳相邻编码树单元,并利用最佳相邻编码树单元的深度,提前预判当前编码树单元的深度搜索范围。同时根据前一帧中相邻编码单元的深度关系和当前帧中已编码相邻编码单元的深度,预判当前编码单元的深度搜索范围,从而进一步提高了帧间编码单元选择的速度。 HEVC中的多参考帧技术以及灵活的数据划分方式,大幅度增加了运动估计的复杂度。针对多参考帧选择,提出了基于不同预测单元最佳参考帧相关性和层间编码单元最佳参考帧相关性的多参考帧选择算法。利用划分为2N×2N的预测单元中各个参考帧的率失真代价,减少同一编码单元中其它划分模式的候选参考帧数目,加速参考帧选择过程。同时当父编码单元的模式为SKIP时,将当前编码单元中所有划分模式的参考帧限定为父编码单元的最佳参考帧,从而进一步降低多参考帧选择的复杂度。另一方面,搜索范围在影响运动搜索复杂度的同时也影响数据搬运带宽,通过分析不同分辨率视频设置不同搜索范围的编码结果,为不同分辨率的视频推荐不同的搜索范围,能够有效的降低数据搬运带宽。最后总结了本论文的研究成果,并提出了该领域下一步研究的方向和任务。
[Abstract]:The coding efficiency of the new generation video coding standard HEVC (High Efficiency Video Coding) is more than double that of H.264/MPEG-4AVC. However, the flexible choice of encoding tools makes the complexity of HEVC encoder increase dramatically, which seriously hinders the application and development of HEVC. So it is very important to study the fast algorithm of HEVC coding. After introducing the general situation of video coding technology and the development history of video coding standards, this paper summarizes the coding framework of HEVC. Then, according to the characteristics of HEVC coding tools, the paper points out the direction of HEVC coding optimization: fast selection algorithm of intra coding unit, fast selection algorithm of interframe coding unit and fast algorithm of motion estimation. In order to solve the problem of high complexity of HEVC intra coding unit partitioning, a fast selection algorithm of intra coding unit based on statistical learning is proposed. The partition of intra coding units is modeled as k-means classification problem. By analyzing the characteristics of four dimensional vectors formed by the variance combination of four subcoding units covering the region pixels, a simple and effective k-means classification method is used to predict the division of coding units. Thus the full search algorithm based on rate-distortion optimization is avoided and the computational complexity of encoder is reduced. In order to solve the problem of high complexity of HEVC inter-frame coding unit partition, a fast selection algorithm of inter-frame coding unit based on depth space-time correlation is proposed. After analyzing the correlation between the adjacent coding tree units in time and space, the best adjacent coding tree units are selected according to the relative strength, and the depth of the best adjacent coding tree units is determined in advance to determine the depth search range of the current coding tree units. At the same time, according to the depth relation of adjacent coding unit in the previous frame and the depth of the adjacent coding unit in the current frame, the depth search range of the current coding unit is forecasted. The multi-reference frame technology in HEVC and the flexible data partition method greatly increase the complexity of motion estimation. For multi-reference frame selection, a multi-reference frame selection algorithm based on optimal reference frame correlation of different prediction units and optimal reference frame correlation of interlayer coding unit is proposed. Using the rate-distortion cost of each reference frame in the prediction unit divided into 2N 脳 2N, the number of candidate reference frames in other partition modes in the same coding unit is reduced, and the selection process of reference frames is accelerated. At the same time, when the mode of the parent coding unit is SKIP, the reference frames of all partition modes in the current coding unit are limited to the best reference frames of the parent coding unit, thus further reducing the complexity of multi-reference frame selection. On the other hand, the search range not only affects the complexity of motion search, but also affects the bandwidth of data transportation. By analyzing the coding results of different search range for different resolution video, it recommends different search range for different resolution video. Can effectively reduce the data handling bandwidth. Finally, the research results of this paper are summarized, and the next research directions and tasks in this field are proposed.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN919.81
本文编号：2176154
[Abstract]:The coding efficiency of the new generation video coding standard HEVC (High Efficiency Video Coding) is more than double that of H.264/MPEG-4AVC. However, the flexible choice of encoding tools makes the complexity of HEVC encoder increase dramatically, which seriously hinders the application and development of HEVC. So it is very important to study the fast algorithm of HEVC coding. After introducing the general situation of video coding technology and the development history of video coding standards, this paper summarizes the coding framework of HEVC. Then, according to the characteristics of HEVC coding tools, the paper points out the direction of HEVC coding optimization: fast selection algorithm of intra coding unit, fast selection algorithm of interframe coding unit and fast algorithm of motion estimation. In order to solve the problem of high complexity of HEVC intra coding unit partitioning, a fast selection algorithm of intra coding unit based on statistical learning is proposed. The partition of intra coding units is modeled as k-means classification problem. By analyzing the characteristics of four dimensional vectors formed by the variance combination of four subcoding units covering the region pixels, a simple and effective k-means classification method is used to predict the division of coding units. Thus the full search algorithm based on rate-distortion optimization is avoided and the computational complexity of encoder is reduced. In order to solve the problem of high complexity of HEVC inter-frame coding unit partition, a fast selection algorithm of inter-frame coding unit based on depth space-time correlation is proposed. After analyzing the correlation between the adjacent coding tree units in time and space, the best adjacent coding tree units are selected according to the relative strength, and the depth of the best adjacent coding tree units is determined in advance to determine the depth search range of the current coding tree units. At the same time, according to the depth relation of adjacent coding unit in the previous frame and the depth of the adjacent coding unit in the current frame, the depth search range of the current coding unit is forecasted. The multi-reference frame technology in HEVC and the flexible data partition method greatly increase the complexity of motion estimation. For multi-reference frame selection, a multi-reference frame selection algorithm based on optimal reference frame correlation of different prediction units and optimal reference frame correlation of interlayer coding unit is proposed. Using the rate-distortion cost of each reference frame in the prediction unit divided into 2N 脳 2N, the number of candidate reference frames in other partition modes in the same coding unit is reduced, and the selection process of reference frames is accelerated. At the same time, when the mode of the parent coding unit is SKIP, the reference frames of all partition modes in the current coding unit are limited to the best reference frames of the parent coding unit, thus further reducing the complexity of multi-reference frame selection. On the other hand, the search range not only affects the complexity of motion search, but also affects the bandwidth of data transportation. By analyzing the coding results of different search range for different resolution video, it recommends different search range for different resolution video. Can effectively reduce the data handling bandwidth. Finally, the research results of this paper are summarized, and the next research directions and tasks in this field are proposed.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN919.81
【引证文献】
相关硕士学位论文 前1条
1 党允舒;基于能量集中的MVM超平面研究[D];吉林大学;2015年
，本文编号：2176154
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