监控视频编码与超分辨率重建方法研究
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摘要
随着视频监控市场的迅速增长、网络视频监控系统的日益普及,迫切需要对海量数字化监控视频进行压缩存储和后处理与分析。高效率的视频编码技术是解决这些问题的关键。然而在视频监控系统的实际应用中产生了许多问题,主要包括以下三个方面：第一,当视频序列受到各类环境引起噪声的影响时,视频编码的效率明显下降；第二,虽然H.264／AVC等新一代视频编码标准获得了较高的压缩效率,但其编码控制模型非常复杂,至今还难以在硬件编码端上实现其全部特性；第三,由于新一代标准的复杂度较高,给视频监控系统的后处理和分析带来了更高的难度。本文围绕着实施监控视频编码方案时所遇到的编码噪声鲁棒问题、在计算资源受限系统上编码器的复杂度和编码速度问题、以及压缩视频后处理增强问题,开展了相关研究。
本文研究了视频编码的基本原理和基于新一代混合编码框架的视频编码技术,包括我国针对视频监控系统制定的AVS-S标准中的关键技术。指出了目前研究的缺陷和遗漏之处,并说明了本文研究范围与这些技术的区别。
监控视频序列中的噪声对H.264编码模型产生了影响,帧间预测编码受噪声干扰后的不准确性是带来其编码效率下降的根本原因。提出了一种基于联合匹配准则和运动矢量场时空滤波的宏块预分类方法。首先利用对噪声不敏感的匹配准则初步判断当前宏块的运动状态。其次对已编码帧的运动矢量场进行时域和空域滤波,消除噪声运动矢量并获得更能精确描述当前运动物体的运动矢量场。最后根据当前编码宏块的运动状态限定其编码模式。通过改进的匹配准则和运动矢量场滤波两方面帮助编码器提高其噪声鲁棒性,而且算法计算复杂度较小。对典型监控序列的仿真实验表明,本文算法同H.264中的高复杂度率失真优化算法相比较,获得了平均超过0.08dB的性能增益,并平均节约了1.67%的码流,与此同时还节约了平均62.86%的总编码时间。实验证明本文算法具有一定的性能优势和较大的编码速度优势。
本文在总结当前快速算法研究现状的基础上,发现目前快速算法的缺陷在于算法复杂度过高,从而给编码器的硬件设计带来了更大负担。为了降低在硬件上实现新一代混合编码框架的难度,提出了一种基于统计判决的快速冗余预测模式消除方法。在遵循现有编码流程的基础上,通过提取部分已编码模式的编码块模式和帧间预测开销作为辅助判断信息,将各模式编码预测开销之间的比值进行了统计模型化,并作为是否采取当前编码模式的依据。利用统计分布规律快速消除可能性较低的编码模式。本文算法以极低的计算复杂度优化了H.264模型的编码流程。通过对国际通用视频序列的实验表明,本文算法在平均综合性能仅仅损失了0.037dB的情况下,整体编码时间缩减了12.3%。该方法几乎没有增加任何内存开销,为编码器整体框架优化提供了全新的解决思路。
在压缩视频超分辨率重建方法方面,本文在总结了当前基于压缩视频超分辨率重建研究现状的基础上,指出现有基于MPEG域的方法难以运用到以H.264为代表的新一代视频编码技术上。另外本文研究了目前较好的一种空域变像元超分辨率重建模型,指出其更加适合于同一场景下视频序列的超分辨率重建。最后提出了一种基于宏块多重描述的变像元超分辨率重建算法,通过在编码端保存有效的宏块多重描述信息并传输至解码端,其中包括宏块在相邻帧之间的重要位移信息。在解码端首先提取宏块多重信息中的模式和运动矢量信息,在进行精确匹配和定位之后将相邻重建帧上的对应整像素点投影到超分辨率重建图像上,完成解码端视频图像的增强。分别通过对空间分辨率、客观重建性能和解码端视频序列超分辨率重建的实验证明了本文算法在主客观性能上有着较好表现。本文算法实际上为压缩视频后处理和分析提供了一种全新的解决思路,即在编码端对感兴趣的运动物体信息进行多重描述以便解码端进行后处理和分析。
本文对面向视频监控的视频编码技术进行了比较全面和深入的研究,提出了一套监控视频编码的解决方案。编码端方面取得的研究成果对增强编码器的噪声鲁棒性和提高编码效率有一定的理论研究意义和实际应用价值。解码端视频后处理方面取得的研究成果则可以较好地解决压缩视频增强的问题,改善了解码图像质量。
With the rapid growth of video surveillance market and network video monitoring system, compression and post-processing analysis of massive digital surveillance video become more and more important. High efficient video coding technology is the key to solve these problems. However, there appeared many problems in the practical application of video surveillance system. First, when video noise increases, the video coding efficiency considerably decreases. Second, the new generation of video coding standard such as H.264/AVC can obtain higher compression efficiency, but it has a very complex coding control model and is difficult for hardware implementation. Third, as the complexity of the new generation of video coding standard is very high, the post-processing and analysis of video surveillance system become more and more difficult. So this thesis is focus on three problems:the noise robustness of video encoder, the complexity of video encoder in the resource-constrained system, compressed video post-processing and enhancement. The main contributions of this thesis are as follows:
First, the basic theoretical knowledge of video coding and hybrid coding framework is deeply introduced. And the key technology of China's AVS-S standard which developed for video surveillance system is described. The dissertation pointed out the insufficiencies and omissions of the current research, and described the scope of this research.
Second, a noise robustness video coding method for video surveillance system is proposed. By analyzing the noise impact of the H.264 coding model, the thesis pointed out the video coding efficiency decreases caused by the inter-frame predictive coding accuracy rate has dropped. And this paper presents a pre-classification method based on co-matching criteria and motion vector field spatial and temporal filtering. The method uses the co-matching criteria to judge the current macroblock in order to eliminate the noise impact and use the temporal and spatial filtering of the motion vector field of encoded frames to eliminate the noise motion vectors. Finally, according to the motion information of current macroblock, the method limits the coding mode of current macroblock. The proposed algorithm can improve the noise robustness of the H.264 encoder. Simulations show that this approach can result in a time savings of over 62.86% for several typical surveillance sequences. And it also reduces the average Bjontegaard delta bit rate by about 1.67% and increases the average Bjontegaard delta peak signal-to-noise ratio by about 0.08dB when compared with the algorithm of H.264. Experiments prove that this algorithm improves the coding performance and coding speed.
Third, this thesis summarizes researches of fast algorithms of H.264 and finds out that many fast algorithms bring a higher computational complexity for the video encoder. In order to reduce the difficulty of the hardware realization of the new generation hybrid coding framework, this thesis propose a fast method based on statistical judgments for fast elimination of redundant prediction modes. This method use the coded block pattern and inter prediction coding cost as the auxiliary information to determine whether to take the current coding mode. And it also uses the statistical distribution for rapid elimination of the coding modes with low probability. This algorithm has a very low computational complexity and optimized the H.264 encoding process. Simulation results reveal that the proposed algorithm can reduce the encoding time by 12.3% on average with the limited performance loss of about 0.037dB. The memory using of this method is very small. And it provides some novel thoughts for designing the video encoder.
Finally, a compressed video super-resolution reconstruction algorithm is presented. This paper summarizes researches on compressed video super-resolution reconstruction and points out that the existing MPEG-based approaches are difficult to apply to the new generation video coding technology. The proposed algorithm is based on the variable-pixel reconstruction algorithm which is more suitable for super-resolution reconstruction of video sequence. The algorithm collects the effective macroblock coding side information on the encoder and retains the macroblock information between adjacent frames. In the decoder side, it uses the macroblock mode and motion vector information of the macroblock coding side information to projected the pixels of the adjacent reconstruction frames onto the super-resolution reconstruction image. The spatial resolution, objective performance, and the decoder reconstruction of super-resolution reconstruction of video sequence experiments proved that this algorithm has a better performance of the objective and subjective performance. And this algorithm provides a novel solution idea for compressed video post-processing and analysis:to save the information of the moving object in encoder side for the post-processing and analysis of decoder side.
To sum up, this paper digs into the video coding technology for video surveillance system and proposed a set of surveillance video coding solution. On the encoder side, the solution enhanced the encoder noise robustness and improved the coding efficiency. And on the decoder side, it can effectively solve the issue of compressed video enhancement.
本文研究了视频编码的基本原理和基于新一代混合编码框架的视频编码技术,包括我国针对视频监控系统制定的AVS-S标准中的关键技术。指出了目前研究的缺陷和遗漏之处,并说明了本文研究范围与这些技术的区别。
监控视频序列中的噪声对H.264编码模型产生了影响,帧间预测编码受噪声干扰后的不准确性是带来其编码效率下降的根本原因。提出了一种基于联合匹配准则和运动矢量场时空滤波的宏块预分类方法。首先利用对噪声不敏感的匹配准则初步判断当前宏块的运动状态。其次对已编码帧的运动矢量场进行时域和空域滤波,消除噪声运动矢量并获得更能精确描述当前运动物体的运动矢量场。最后根据当前编码宏块的运动状态限定其编码模式。通过改进的匹配准则和运动矢量场滤波两方面帮助编码器提高其噪声鲁棒性,而且算法计算复杂度较小。对典型监控序列的仿真实验表明,本文算法同H.264中的高复杂度率失真优化算法相比较,获得了平均超过0.08dB的性能增益,并平均节约了1.67%的码流,与此同时还节约了平均62.86%的总编码时间。实验证明本文算法具有一定的性能优势和较大的编码速度优势。
本文在总结当前快速算法研究现状的基础上,发现目前快速算法的缺陷在于算法复杂度过高,从而给编码器的硬件设计带来了更大负担。为了降低在硬件上实现新一代混合编码框架的难度,提出了一种基于统计判决的快速冗余预测模式消除方法。在遵循现有编码流程的基础上,通过提取部分已编码模式的编码块模式和帧间预测开销作为辅助判断信息,将各模式编码预测开销之间的比值进行了统计模型化,并作为是否采取当前编码模式的依据。利用统计分布规律快速消除可能性较低的编码模式。本文算法以极低的计算复杂度优化了H.264模型的编码流程。通过对国际通用视频序列的实验表明,本文算法在平均综合性能仅仅损失了0.037dB的情况下,整体编码时间缩减了12.3%。该方法几乎没有增加任何内存开销,为编码器整体框架优化提供了全新的解决思路。
在压缩视频超分辨率重建方法方面,本文在总结了当前基于压缩视频超分辨率重建研究现状的基础上,指出现有基于MPEG域的方法难以运用到以H.264为代表的新一代视频编码技术上。另外本文研究了目前较好的一种空域变像元超分辨率重建模型,指出其更加适合于同一场景下视频序列的超分辨率重建。最后提出了一种基于宏块多重描述的变像元超分辨率重建算法,通过在编码端保存有效的宏块多重描述信息并传输至解码端,其中包括宏块在相邻帧之间的重要位移信息。在解码端首先提取宏块多重信息中的模式和运动矢量信息,在进行精确匹配和定位之后将相邻重建帧上的对应整像素点投影到超分辨率重建图像上,完成解码端视频图像的增强。分别通过对空间分辨率、客观重建性能和解码端视频序列超分辨率重建的实验证明了本文算法在主客观性能上有着较好表现。本文算法实际上为压缩视频后处理和分析提供了一种全新的解决思路,即在编码端对感兴趣的运动物体信息进行多重描述以便解码端进行后处理和分析。
本文对面向视频监控的视频编码技术进行了比较全面和深入的研究,提出了一套监控视频编码的解决方案。编码端方面取得的研究成果对增强编码器的噪声鲁棒性和提高编码效率有一定的理论研究意义和实际应用价值。解码端视频后处理方面取得的研究成果则可以较好地解决压缩视频增强的问题,改善了解码图像质量。
With the rapid growth of video surveillance market and network video monitoring system, compression and post-processing analysis of massive digital surveillance video become more and more important. High efficient video coding technology is the key to solve these problems. However, there appeared many problems in the practical application of video surveillance system. First, when video noise increases, the video coding efficiency considerably decreases. Second, the new generation of video coding standard such as H.264/AVC can obtain higher compression efficiency, but it has a very complex coding control model and is difficult for hardware implementation. Third, as the complexity of the new generation of video coding standard is very high, the post-processing and analysis of video surveillance system become more and more difficult. So this thesis is focus on three problems:the noise robustness of video encoder, the complexity of video encoder in the resource-constrained system, compressed video post-processing and enhancement. The main contributions of this thesis are as follows:
First, the basic theoretical knowledge of video coding and hybrid coding framework is deeply introduced. And the key technology of China's AVS-S standard which developed for video surveillance system is described. The dissertation pointed out the insufficiencies and omissions of the current research, and described the scope of this research.
Second, a noise robustness video coding method for video surveillance system is proposed. By analyzing the noise impact of the H.264 coding model, the thesis pointed out the video coding efficiency decreases caused by the inter-frame predictive coding accuracy rate has dropped. And this paper presents a pre-classification method based on co-matching criteria and motion vector field spatial and temporal filtering. The method uses the co-matching criteria to judge the current macroblock in order to eliminate the noise impact and use the temporal and spatial filtering of the motion vector field of encoded frames to eliminate the noise motion vectors. Finally, according to the motion information of current macroblock, the method limits the coding mode of current macroblock. The proposed algorithm can improve the noise robustness of the H.264 encoder. Simulations show that this approach can result in a time savings of over 62.86% for several typical surveillance sequences. And it also reduces the average Bjontegaard delta bit rate by about 1.67% and increases the average Bjontegaard delta peak signal-to-noise ratio by about 0.08dB when compared with the algorithm of H.264. Experiments prove that this algorithm improves the coding performance and coding speed.
Third, this thesis summarizes researches of fast algorithms of H.264 and finds out that many fast algorithms bring a higher computational complexity for the video encoder. In order to reduce the difficulty of the hardware realization of the new generation hybrid coding framework, this thesis propose a fast method based on statistical judgments for fast elimination of redundant prediction modes. This method use the coded block pattern and inter prediction coding cost as the auxiliary information to determine whether to take the current coding mode. And it also uses the statistical distribution for rapid elimination of the coding modes with low probability. This algorithm has a very low computational complexity and optimized the H.264 encoding process. Simulation results reveal that the proposed algorithm can reduce the encoding time by 12.3% on average with the limited performance loss of about 0.037dB. The memory using of this method is very small. And it provides some novel thoughts for designing the video encoder.
Finally, a compressed video super-resolution reconstruction algorithm is presented. This paper summarizes researches on compressed video super-resolution reconstruction and points out that the existing MPEG-based approaches are difficult to apply to the new generation video coding technology. The proposed algorithm is based on the variable-pixel reconstruction algorithm which is more suitable for super-resolution reconstruction of video sequence. The algorithm collects the effective macroblock coding side information on the encoder and retains the macroblock information between adjacent frames. In the decoder side, it uses the macroblock mode and motion vector information of the macroblock coding side information to projected the pixels of the adjacent reconstruction frames onto the super-resolution reconstruction image. The spatial resolution, objective performance, and the decoder reconstruction of super-resolution reconstruction of video sequence experiments proved that this algorithm has a better performance of the objective and subjective performance. And this algorithm provides a novel solution idea for compressed video post-processing and analysis:to save the information of the moving object in encoder side for the post-processing and analysis of decoder side.
To sum up, this paper digs into the video coding technology for video surveillance system and proposed a set of surveillance video coding solution. On the encoder side, the solution enhanced the encoder noise robustness and improved the coding efficiency. And on the decoder side, it can effectively solve the issue of compressed video enhancement.
引文
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