H.264到HEVC视频转码技术研究
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摘要
视频编码技术发展的趋势之一是追求更高的编码效率。H.264视频编码标准在提高编码效率和灵活性方面取得了巨大成功,它使得数字视频有效地应用于各种各样的网络类型和工程领域。然而,多样化的服务、高清视频的普及、以及超高清格式(4K×2K或8K×4K分辨率)的出现对于比H.264编码效率更高的下一代视频编码标准提出了强烈的需求。在这样的背景下,MPEG和VCEG组织于2010年成立了视频编码联合协作小组(JCT-VC),经过多年的努力,研发出了H.264标准的继承者,新一代视频编码标准HEVC。与H.264相比,HEVC虽然可以在相似的视频感知质量下节省高达约50%的比特率,但由于H.264广泛而深入的应用,在相当长一段时间内,这两个技术需要共存。因此H.264到HEVC的转码在网络传输和存储方面具有重要的现实意义。然而,HEVC为了提高编码效率,引入了一系列相当耗时的编码算法,给实时视频转码应用带来了新的挑战。针对HEVC编码算法特性,在转码过程中充分利用H.264码流信息来加速转码中HEVC重编码过程是提高转码器性能的关键之一。此外,由于转码的目标是为了在同样的视频质量下获取更高的压缩效率,而视觉显著性分析已成为计算机视觉和图像处理领域一个重要的研究课题,因此如何从人眼视觉感知的角度,在H.264码流压缩域提取视觉显著性进而指导H.264到HEVC的转码过程也成为提高转码效率的关键之一。正是在这样的背景下,本文展开了对H.264到HEVC视频转码方法的研究。
第一章首先阐述了选题的意义,接着对视频编解码技术、H.264和HEVC编码技术、视频转码技术以及视觉显著性及其应用进行了简单综述,最后介绍了本文的主要研究内容和论文结构。
第二章从统计分析的角度对H.264到]HEVC快速视频转码方法进行研究。针对帧间转码,首先通过大量统计分析找出HEVC码流中Skip模式与H.264码流中各种模式的映射关系,并利用其对Skip模式进行提前判决,然后通过对编码比特数进行数理统计分析快速选择预测单元的对称与非对称分割模式,最后依据运动矢量的相似性优化了HEVC运动估计过程中预测单元的搜索起点和搜索范围,进一步减少了转码过程的计算量。针对帧内转码,首先利用H.264和HEVC帧内编码模式之间的关系自适应地选择编码树单元的搜索深度范围,然后通过计算编码单元区域的梯度大小和方向,找出其与帧内方向预测模式之间的关系,减少HEVC帧内预测的候选模式集中的模式个数,进而加速帧内转码过程。
第三章提出了一种基于区域特征分析的H.264到HEVC快速视频转码方法。该方法首先根据图像复杂度和编码比特数之间的关系将每帧图像以编码树单元为单位划分为三种复杂度区域,其次按照不同区域类型决定每个编码树单元的搜索深度范围。然后对运动矢量进行去噪滤波和聚类以分析每个编码单元的区域特征,依据其分析结果择优选择编码单元的最小搜索深度和预测单元的最可能分割模式。最后,将聚类中心的运动矢量加入预测运动矢量作为候选,从而减小运动搜索窗的大小,以达到在保证几乎相同的率失真视频质量下,大幅减少转码过程的计算复杂度。
第四章针对H.264到HEVC视频转码效率进行研究,通过分析视觉显著性及其在视频转码领域中的应用,根据人眼视觉的特点,提出一种基于视觉显著性分析的H.264到HEVC视频转码算法。该方法首先利用H.264码流中的运动矢量场进行全局运动估计和局部运动分割得到运动显著性图,然后结合编码比特数的分布特点加以修正生成最终的视觉显著性图,最后在HEVC重编码过程中,利用视觉显著性图对非显著性区域进行自适应频率系数压制,以在保持视频主观质量的前提下,进一步提高视频转码的效率。
第五章总结归纳了本文的创新点和研究成果并提出了进一步的研究方向和任务。
One of the development trend in video coding technology is the pursuit of higher coding efficiency. The H.264standard has been a great success in terms of both coding efficiency enhancement and flexibility for effective use over a broad variety of network and application domains. However, an increasing diversity of services, the growing popularity of HD video, and the emergence of beyond-HD formats (e.g.4k×2k or8k×4k resolution) are creating even stronger needs for coding efficiency superior to H.264's capabilities. Under this circumstance, MPEG and VCEG have established a Joint Collaborative Team on Video Coding (JCT-VC) to develop a successor to H.264. This new international standard is referred to as High Efficiency Video Coding (HEVC). Although HEVC can achieve up to approximately50%bit rate savings for similar perceived video quality compared to H.264, the wide and deep penetration of H.264creates a need for the co-existence of these technologies in a fairly long period of time. Therefore, transcoding from H.264to HEVC has important practical significance in the network transmission and storage. However, on one hand, HEVC introduces a series of time-consuming coding algorithm in order to improve the coding efficiency, thus brings new challenges to real-time video transcoding applications. Considering the characteristics of HEVC coding algorithm, making full use of the H.264bitstream information to speed up the HEVC re-encoding process of transcoding is one of the key technologies to improve the performance of transcoder. On the other hand, since the goal of transcoding is to obtain higher compression efficiency at the same video quality and the analysis of video saliency in computer vision and image processing has become an important research field, how to extract the video saliency from the compress domain of H.264bitstream from a human visual perception to guide the optimization of the transcoding from H.264to HEVC is also one of the key technologies to improve the performance of transcoder. In this context, the research of video transcoding from H.264to HEVC is carried out.
In chapter1, the importance of the research work is firstly presented. Secondly, the video codec technology, H.264and HEVC codec technology, video transcoding technology and video saliency and its applications are biefly summarized. Finally, the main research contents and the structure of the thesis are illustrated.
In chapter2, low-complexity video transcoding algorithm from H.264to HEVC is studied based on statistical analysis. For inter frame transcoding, by exploiting the mapping correlations of Skip mode in HEVC and all modes in H.264/AVC, an early decision of Skip mode is firstly introduced. Secondly, through the statistical analysis of coding bits, a fast prediction unit (PU) partition selection for both symmetric and asymmetric partitions is described. Finally, the motion estimation process is optimized according to the motion similarity between H.264and HEVC bitstreams. For intra frame transcoding, the searching depth range of coding tree unit (CTU) is firstly decided based on the the intra coding modes in H.264. Secondly, gradient directions are statistically calculated and a gradient-mode histogram is generated for each coding unit. Finally, based on the distribution of the histogram, only a small part of the candidate modes are chosen for the intra coding processes.
In chapter3, a fast H.264to HEVC transcoding algorithm based on region feature analysis is proposed. First, each frame is segmented into three regions in units of CTU based on the correlation between image coding complexities and coding bits of the H.264source stream. Then the searching depth range of each CTU is adaptively decided according to the region type. After that, motion vectors are de-noise filtered and clustered in order to analyze the region features of coding unit (CU). Based on the analysis results, the minimum searching depth of CU and partitions of PU are optimally selected, and the motion vector predictor and search window size of motion estimation are also optimally decided for further reduction of the computational complexity.
In chapter4, based on the characteristics of human vision system, a video saliency based video transcoding algorithm from H.264to HEVC is proposed. Firstly, the motion vector field included in the H.264bitstream is utilized to do the global motion estimation and motion segmentation. Secondly, the distribution of coding bits is combined to produce the visual saliency map which indicates the salient regions in the videos. Finally, during the re-encoding process, a frequency coefficient suppression technique in the transform domain is used in the non salient region for further bitrate reduction while keeping the subjective quality of salient region.
In the final chapter, the novel achievements of the research in this thesis and the prospect of the future research are concluded.
第一章首先阐述了选题的意义,接着对视频编解码技术、H.264和HEVC编码技术、视频转码技术以及视觉显著性及其应用进行了简单综述,最后介绍了本文的主要研究内容和论文结构。
第二章从统计分析的角度对H.264到]HEVC快速视频转码方法进行研究。针对帧间转码,首先通过大量统计分析找出HEVC码流中Skip模式与H.264码流中各种模式的映射关系,并利用其对Skip模式进行提前判决,然后通过对编码比特数进行数理统计分析快速选择预测单元的对称与非对称分割模式,最后依据运动矢量的相似性优化了HEVC运动估计过程中预测单元的搜索起点和搜索范围,进一步减少了转码过程的计算量。针对帧内转码,首先利用H.264和HEVC帧内编码模式之间的关系自适应地选择编码树单元的搜索深度范围,然后通过计算编码单元区域的梯度大小和方向,找出其与帧内方向预测模式之间的关系,减少HEVC帧内预测的候选模式集中的模式个数,进而加速帧内转码过程。
第三章提出了一种基于区域特征分析的H.264到HEVC快速视频转码方法。该方法首先根据图像复杂度和编码比特数之间的关系将每帧图像以编码树单元为单位划分为三种复杂度区域,其次按照不同区域类型决定每个编码树单元的搜索深度范围。然后对运动矢量进行去噪滤波和聚类以分析每个编码单元的区域特征,依据其分析结果择优选择编码单元的最小搜索深度和预测单元的最可能分割模式。最后,将聚类中心的运动矢量加入预测运动矢量作为候选,从而减小运动搜索窗的大小,以达到在保证几乎相同的率失真视频质量下,大幅减少转码过程的计算复杂度。
第四章针对H.264到HEVC视频转码效率进行研究,通过分析视觉显著性及其在视频转码领域中的应用,根据人眼视觉的特点,提出一种基于视觉显著性分析的H.264到HEVC视频转码算法。该方法首先利用H.264码流中的运动矢量场进行全局运动估计和局部运动分割得到运动显著性图,然后结合编码比特数的分布特点加以修正生成最终的视觉显著性图,最后在HEVC重编码过程中,利用视觉显著性图对非显著性区域进行自适应频率系数压制,以在保持视频主观质量的前提下,进一步提高视频转码的效率。
第五章总结归纳了本文的创新点和研究成果并提出了进一步的研究方向和任务。
One of the development trend in video coding technology is the pursuit of higher coding efficiency. The H.264standard has been a great success in terms of both coding efficiency enhancement and flexibility for effective use over a broad variety of network and application domains. However, an increasing diversity of services, the growing popularity of HD video, and the emergence of beyond-HD formats (e.g.4k×2k or8k×4k resolution) are creating even stronger needs for coding efficiency superior to H.264's capabilities. Under this circumstance, MPEG and VCEG have established a Joint Collaborative Team on Video Coding (JCT-VC) to develop a successor to H.264. This new international standard is referred to as High Efficiency Video Coding (HEVC). Although HEVC can achieve up to approximately50%bit rate savings for similar perceived video quality compared to H.264, the wide and deep penetration of H.264creates a need for the co-existence of these technologies in a fairly long period of time. Therefore, transcoding from H.264to HEVC has important practical significance in the network transmission and storage. However, on one hand, HEVC introduces a series of time-consuming coding algorithm in order to improve the coding efficiency, thus brings new challenges to real-time video transcoding applications. Considering the characteristics of HEVC coding algorithm, making full use of the H.264bitstream information to speed up the HEVC re-encoding process of transcoding is one of the key technologies to improve the performance of transcoder. On the other hand, since the goal of transcoding is to obtain higher compression efficiency at the same video quality and the analysis of video saliency in computer vision and image processing has become an important research field, how to extract the video saliency from the compress domain of H.264bitstream from a human visual perception to guide the optimization of the transcoding from H.264to HEVC is also one of the key technologies to improve the performance of transcoder. In this context, the research of video transcoding from H.264to HEVC is carried out.
In chapter1, the importance of the research work is firstly presented. Secondly, the video codec technology, H.264and HEVC codec technology, video transcoding technology and video saliency and its applications are biefly summarized. Finally, the main research contents and the structure of the thesis are illustrated.
In chapter2, low-complexity video transcoding algorithm from H.264to HEVC is studied based on statistical analysis. For inter frame transcoding, by exploiting the mapping correlations of Skip mode in HEVC and all modes in H.264/AVC, an early decision of Skip mode is firstly introduced. Secondly, through the statistical analysis of coding bits, a fast prediction unit (PU) partition selection for both symmetric and asymmetric partitions is described. Finally, the motion estimation process is optimized according to the motion similarity between H.264and HEVC bitstreams. For intra frame transcoding, the searching depth range of coding tree unit (CTU) is firstly decided based on the the intra coding modes in H.264. Secondly, gradient directions are statistically calculated and a gradient-mode histogram is generated for each coding unit. Finally, based on the distribution of the histogram, only a small part of the candidate modes are chosen for the intra coding processes.
In chapter3, a fast H.264to HEVC transcoding algorithm based on region feature analysis is proposed. First, each frame is segmented into three regions in units of CTU based on the correlation between image coding complexities and coding bits of the H.264source stream. Then the searching depth range of each CTU is adaptively decided according to the region type. After that, motion vectors are de-noise filtered and clustered in order to analyze the region features of coding unit (CU). Based on the analysis results, the minimum searching depth of CU and partitions of PU are optimally selected, and the motion vector predictor and search window size of motion estimation are also optimally decided for further reduction of the computational complexity.
In chapter4, based on the characteristics of human vision system, a video saliency based video transcoding algorithm from H.264to HEVC is proposed. Firstly, the motion vector field included in the H.264bitstream is utilized to do the global motion estimation and motion segmentation. Secondly, the distribution of coding bits is combined to produce the visual saliency map which indicates the salient regions in the videos. Finally, during the re-encoding process, a frequency coefficient suppression technique in the transform domain is used in the non salient region for further bitrate reduction while keeping the subjective quality of salient region.
In the final chapter, the novel achievements of the research in this thesis and the prospect of the future research are concluded.
引文
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