分布式视频压缩的关键技术研究
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摘要
随着移动互联网和信息技术的发展,日益丰富的移动视频应用正逐渐应用于社会的各个领域并潜移默化地改变人类了的生活方式。但是,传统的视频编码标准,如MPEG/H.26X,由于需要在编码端进行复杂的运动估计和运动补偿预测,因此编码端的复杂度一般为译码端的5到10倍以上。这对于处理能力和能量有限的移动终端而言是很难接受的,将极大程度上增加移动终端的成本。同时传统编码标准对预测帧的依赖性也使得其在无线网络中容错能力较差,无法满足人们对高质量视频的要求。因此,设计出一个编码端复杂度低和容错能力强的视频编码方案,从根本上改变现有的视频编码标准,成为移动视频应用中目前亟待解决的问题。
针对上述需求,具有低复杂度压缩特性及鲁棒性的分布式视频压缩技术受到了广泛的关注与研究。分布式视频压缩技术通过对视频信号的独立编码和联合译码,在摒弃了编码端视频信号对预测帧依赖性的同时,将计算复杂度由编码端转移到了译码端,从而有效地解决了上述问题。因此,本文将从分布式视频编码(Distributed Video Coding, DVC)和分布式压缩视频感知(Distributed Compressive Video sensing, DCVS)两个方面来对分布式视频压缩算法进行研究。具体的研究内容如下所示：
1.在DVC框架下利用已译码信息进行边信息及相关性模型参数的修正。由于视频信号的部分译码信息能够在一定程度上表征视频信号的特性,因此本文一方面提出利用视频帧内的空间相关性,将视频帧按某种规则进行划分,以子帧为单位进行编译码,并根据译码后的子帧对边信息及相关模型的参数进行修正；另一方面提出在子帧的译码过程中可利用已译码比特平面对边信息及相关模型的参数进行修正。经过仿真验证,本文所提出的边信息修正方法和相关模型参数修正方法获得了较好的率失真性能。
2.在DVC中,实现对译码算法的改进。该译码算法利用边信息帧中目标的运动状态对视频块进行了可靠性的划分,将视频块划分为三种模式：零模式,传统模式和反转比特模式。编码器根据块模式信息,对不同模式中视频块量化后系数的二进制表示进行相应处理,并进行了比特平面的提取；译码器根据块模式信息,对比特平面内不同模式下的比特采用不同计算方法以得到相应的似然比初始化值,并利用置信传播方式进行迭代译码；重构单元根据块模式信息对不同模式下译码后的比特进行重组,实现对视频帧的重构。经过仿真证明,改进后译码算法的率失真性能要明显要高于传统的译码算法。
3.在DVC中,设计出一种译码端码率估计方法。针对斯坦福大学研究小组提出的像素域DVC框架中,存在反馈次数过多和译码端复杂度较高的问题,本文提出了一种译码端码率估计的方法。该方法将信道调制方式选择中的错误估计码与DVC中的低密度校验(Low-Density Parity-Check, LDPC)矩阵相结合,在未增加额外码率冗余的情况下,利用生成的校验比特实现了对比特平面的误比特率(Bit Error Rate, BER)估计。随后,利用比特平面的条件熵计算出编码端所需发送的最小校验比特码率。经过仿真验证,该码率估计方法最大可以节省70%的译码时间。
4.在DVC中,根据视频帧中数据重要性程度的不同,设计出具有不等误差保护功能的校验矩阵。在非规则的LDPC码中,根据置信传播译码时的波浪效应,具有较大度的变量节点因能从校验节点中得到大量的信息而可以被快速准确的译码。因此在该设计方案中,本文利用逐步边增长(Progressive Edge-Growth, PEG)算法生成权值递增的校验(Weight-Increasing Parity-Check, WIPC)矩阵作为LDPC校验矩阵。同时译码端参照译码改进算法中对视频块的划分,将视频块划分为三种模式：快速运动模式、中等运动模式及慢速运动模式。编码器根据块模式信息将不同模式下的比特与校验矩阵中的变量节点相对应：快速运动模式下的比特将被映射至WIPC巨阵中度较大的变量节点；慢速运动模式下的比特将与矩阵中具有较小度的变量节点的对应；中等运动模式下的比特则与其余的变量节点相对应。利用这种映射方法,实现了对数据帧中重要数据的侧重保护,从而提高了系统的率失真性能。
5.在DCVS中,根据视频信号在变换域的稀疏表示特性,设计出具有不等误差保护功能的低密度测量矩阵(Low-Density Sensing matrix, LDSM)。本文将DVC中具有不等误差保护功能的校验矩阵的设计思路进行扩展,延伸至分布式压缩视频感知领域,设计出具有不等误差保护功能的LDSM。在此方案中,本文依然采用PEG算法生成WIPC矩阵并将其作为LDSM,随后将视频信号进行DCT后的系数与LDSM中的变量节点相对应：直流分量和低频分量对应较大度的变量节点；高频分量则对应度较小的变量节点。这种方法实现了对直流和低频分量系数的侧重保护,在信噪比(Signal to Noise Ratio, SNR)较高时具有较好的重建质量。
6.在DVC中,设计出具有不等误差保护功能的调制方式。以基于格雷码的正交幅度调制星座图为例,由于其中每个符号所对应的比特序列中的每个比特位置出错的概率是不相同的,因此本文基于这一思想,提出在对比特平面的校验比特数据进行调制时,可将重要的比特平面所对应的校验比特数据映射至星座图中出错概率较小的比特位置；将较不重要的比特平面所对应的校验比特数据映射至星座图中出错概率较大的比特位置。从而实现了对重要比特平面上数据的保护。
With the development of mobile acknowledge e internet and information technology, more and more mobile video applications are widely used in many aspects of society. However, in the conventional video coding standards, such as MPEG/H.26X, the video encoder needs to make use of the video statistical character and explorer complex motion estimation and compensation solutions for much better rate-distortion (RD) performance. As a result, the computing power of the encoder is typically5to10times more complex than that of the decoder. Therefore, it is unrealistic to the mobile terminals with limit computational capability and energy, and increases the cost for the mobile terminals. In addition, the utilization of prediction frames in the conventional video standards also results in weak resisting-error capability in wireless networks and can not fulfill the requirements for video with high quality. Therefore, it is emergent to design a new video coding architecture with a low-complex encoder and robust transmission capacity to replace the conventional video standards.
Considering these requirements, distributed video compression technologies with low complexity and robustness become a research topic. It could get rid of the prediction frame at the encoder and shift the computing complexity from the encoder to the decoder by separately encoding and jointly decoding the correlated video frames. Therefore, the research work on distributed video compression in this paper will include two aspects:Distributed Video Coding (DVC) and Distributed Compressive Video sensing (DCVS). The specific research contents are shown as follows:
1. In the DVC, realize the side information (SI) revision and the correlation noise model (CNM) parameter revision by using the partially decoded information. Because the decoded information in the video frame could partially represent the video frame character, on one hand we propose to partition the frame into several parts. The encoder will encode every part sequentially and utilize the decoded parts as prior information to revise the SI and CNM parameter; on the other hand we propose to make use of the decoded bitplanes as prior acknowledge to revise the SI the CNM parameter. According to the simulation results, the proposed SI revision method and the CNM parameter method could improve the RD performance.
2. In the DVC, realize an improved decoding scheme in our proposed DVC architecture. Considering the movement of the objects in the SI frame, in this scheme all of blocks in the frame are classified as three modes:Zero Mode (ZM), Classical Mode (CM) and Revise bit order Mode (RM). At the encoder, according to the block mode information, the quantified coefficients are converted to their binary representations with different methods. At the decoder, according to the block mode information, different bits belonging to different block mode in one bitplane will have different likelihood-ratio calculation methods. And the reconstruction unit also utilizes the block mode information to realize the frame reconstruction. Simulation results show that our proposed decoding scheme could achieve better performance than the traditional decoding algorithm.
3. In the DVC, propose a rate estimation method at the decoder. In order to reduce the feedback times and the computing complexity at the decoder, we propose a new decoder rate estimation method. By combining the error estimation code used in the modulation mode selection with the Low-Density Parity-Check (LDPC) matrix in the DVC, this method makes use of the parity bits to estimate the bit error ratio(BER) of the bitplane without increasing any redundant bits. Finally, with the BER the minimum rate of the bitplane is calculated with the conditional entropy. Simulation results indicate that our proposed rate estimation method could save70%decoding time at the maximum.
4. In the DVC, according to the importance of data in the video frame, design an LDPC matrix with unequal error protection (UEP) function. In the irregular LDPC code, according to the wave effect in the belief propagation decoding process, the variable nodes having larger degree could get more reference information from the check node connected to it and could be decoded quickly and accurately. Thus, in this paper, we utilize the Progressive Edge-Growth (PEG) algorithm to generate an Weight-Increasing Parity-Check (WIPC) matrix as the LDPC matrix。Simultaneously, the decoder exploit the same method to partition all of blocks in the frame into three mode:Fast Motion (FM)、Moderate Motion (MM) and Stationary/Slow Motion (SM). The encoder will map the bits belonging to different modes to variable nodes with different degrees in the LDPC matrix for UEP. If the pixels located in the block belonging to FM, the corresponding bits of the pixels will be mapped to the variable nodes with the larger degrees/weights in the WIPC matrix; if the pixels located in the block belonging to SM, the corresponding binary representations of the pixels will be mapped to the variable nodes with the smaller weights; otherwise, the remaining binary representations of the pixels will be mapped to the other variable nodes. With this method, we can realize the UEP and obtain better compression efficiency and decoding performance.
5. In the DCVS, according to the sparsity of the video signal in the transform domain, design a Low-Density Sensing matrix (LDSM) with UEP function. In this paper, we extend the design thought about LDPC matrix with UEP function in DVC to the DCVS and design an LDSM with UEP function. In this paper, we also adopt the PEG algorithm to generate an WIPC matrix as the LDSM. Then the coefficients after discrete cosine transform will be mapped to the variable nodes with different degree in the LDSM. The larger coefficients will correspond to the variable nodes with larger degree and the smaller coefficients will correspond to the variable nodes with smaller degree. With this method, the larger coefficients could be effectively protected and have better recovery quality at low signal to noise ratios.
6. Design a new modulation method with UEP function for DVC Taken a QAM constellation map with Gray code as an example, according to the calculating results, different bit position of the symbol in the constellation map have different priority. Therefore, if we could place the parity bits of more important bitplanes in more protection bit position of the constellation map and place the parity bits of less important bitplanes in less protection bit position, we will realize the UEP of the data bits without consuming any additional resources. The simulation results show that compared with the traditional method in the DVC, PSNR gain for our proposed scheme can reach up to1db.
针对上述需求,具有低复杂度压缩特性及鲁棒性的分布式视频压缩技术受到了广泛的关注与研究。分布式视频压缩技术通过对视频信号的独立编码和联合译码,在摒弃了编码端视频信号对预测帧依赖性的同时,将计算复杂度由编码端转移到了译码端,从而有效地解决了上述问题。因此,本文将从分布式视频编码(Distributed Video Coding, DVC)和分布式压缩视频感知(Distributed Compressive Video sensing, DCVS)两个方面来对分布式视频压缩算法进行研究。具体的研究内容如下所示：
1.在DVC框架下利用已译码信息进行边信息及相关性模型参数的修正。由于视频信号的部分译码信息能够在一定程度上表征视频信号的特性,因此本文一方面提出利用视频帧内的空间相关性,将视频帧按某种规则进行划分,以子帧为单位进行编译码,并根据译码后的子帧对边信息及相关模型的参数进行修正；另一方面提出在子帧的译码过程中可利用已译码比特平面对边信息及相关模型的参数进行修正。经过仿真验证,本文所提出的边信息修正方法和相关模型参数修正方法获得了较好的率失真性能。
2.在DVC中,实现对译码算法的改进。该译码算法利用边信息帧中目标的运动状态对视频块进行了可靠性的划分,将视频块划分为三种模式：零模式,传统模式和反转比特模式。编码器根据块模式信息,对不同模式中视频块量化后系数的二进制表示进行相应处理,并进行了比特平面的提取；译码器根据块模式信息,对比特平面内不同模式下的比特采用不同计算方法以得到相应的似然比初始化值,并利用置信传播方式进行迭代译码；重构单元根据块模式信息对不同模式下译码后的比特进行重组,实现对视频帧的重构。经过仿真证明,改进后译码算法的率失真性能要明显要高于传统的译码算法。
3.在DVC中,设计出一种译码端码率估计方法。针对斯坦福大学研究小组提出的像素域DVC框架中,存在反馈次数过多和译码端复杂度较高的问题,本文提出了一种译码端码率估计的方法。该方法将信道调制方式选择中的错误估计码与DVC中的低密度校验(Low-Density Parity-Check, LDPC)矩阵相结合,在未增加额外码率冗余的情况下,利用生成的校验比特实现了对比特平面的误比特率(Bit Error Rate, BER)估计。随后,利用比特平面的条件熵计算出编码端所需发送的最小校验比特码率。经过仿真验证,该码率估计方法最大可以节省70%的译码时间。
4.在DVC中,根据视频帧中数据重要性程度的不同,设计出具有不等误差保护功能的校验矩阵。在非规则的LDPC码中,根据置信传播译码时的波浪效应,具有较大度的变量节点因能从校验节点中得到大量的信息而可以被快速准确的译码。因此在该设计方案中,本文利用逐步边增长(Progressive Edge-Growth, PEG)算法生成权值递增的校验(Weight-Increasing Parity-Check, WIPC)矩阵作为LDPC校验矩阵。同时译码端参照译码改进算法中对视频块的划分,将视频块划分为三种模式：快速运动模式、中等运动模式及慢速运动模式。编码器根据块模式信息将不同模式下的比特与校验矩阵中的变量节点相对应：快速运动模式下的比特将被映射至WIPC巨阵中度较大的变量节点；慢速运动模式下的比特将与矩阵中具有较小度的变量节点的对应；中等运动模式下的比特则与其余的变量节点相对应。利用这种映射方法,实现了对数据帧中重要数据的侧重保护,从而提高了系统的率失真性能。
5.在DCVS中,根据视频信号在变换域的稀疏表示特性,设计出具有不等误差保护功能的低密度测量矩阵(Low-Density Sensing matrix, LDSM)。本文将DVC中具有不等误差保护功能的校验矩阵的设计思路进行扩展,延伸至分布式压缩视频感知领域,设计出具有不等误差保护功能的LDSM。在此方案中,本文依然采用PEG算法生成WIPC矩阵并将其作为LDSM,随后将视频信号进行DCT后的系数与LDSM中的变量节点相对应：直流分量和低频分量对应较大度的变量节点；高频分量则对应度较小的变量节点。这种方法实现了对直流和低频分量系数的侧重保护,在信噪比(Signal to Noise Ratio, SNR)较高时具有较好的重建质量。
6.在DVC中,设计出具有不等误差保护功能的调制方式。以基于格雷码的正交幅度调制星座图为例,由于其中每个符号所对应的比特序列中的每个比特位置出错的概率是不相同的,因此本文基于这一思想,提出在对比特平面的校验比特数据进行调制时,可将重要的比特平面所对应的校验比特数据映射至星座图中出错概率较小的比特位置；将较不重要的比特平面所对应的校验比特数据映射至星座图中出错概率较大的比特位置。从而实现了对重要比特平面上数据的保护。
With the development of mobile acknowledge e internet and information technology, more and more mobile video applications are widely used in many aspects of society. However, in the conventional video coding standards, such as MPEG/H.26X, the video encoder needs to make use of the video statistical character and explorer complex motion estimation and compensation solutions for much better rate-distortion (RD) performance. As a result, the computing power of the encoder is typically5to10times more complex than that of the decoder. Therefore, it is unrealistic to the mobile terminals with limit computational capability and energy, and increases the cost for the mobile terminals. In addition, the utilization of prediction frames in the conventional video standards also results in weak resisting-error capability in wireless networks and can not fulfill the requirements for video with high quality. Therefore, it is emergent to design a new video coding architecture with a low-complex encoder and robust transmission capacity to replace the conventional video standards.
Considering these requirements, distributed video compression technologies with low complexity and robustness become a research topic. It could get rid of the prediction frame at the encoder and shift the computing complexity from the encoder to the decoder by separately encoding and jointly decoding the correlated video frames. Therefore, the research work on distributed video compression in this paper will include two aspects:Distributed Video Coding (DVC) and Distributed Compressive Video sensing (DCVS). The specific research contents are shown as follows:
1. In the DVC, realize the side information (SI) revision and the correlation noise model (CNM) parameter revision by using the partially decoded information. Because the decoded information in the video frame could partially represent the video frame character, on one hand we propose to partition the frame into several parts. The encoder will encode every part sequentially and utilize the decoded parts as prior information to revise the SI and CNM parameter; on the other hand we propose to make use of the decoded bitplanes as prior acknowledge to revise the SI the CNM parameter. According to the simulation results, the proposed SI revision method and the CNM parameter method could improve the RD performance.
2. In the DVC, realize an improved decoding scheme in our proposed DVC architecture. Considering the movement of the objects in the SI frame, in this scheme all of blocks in the frame are classified as three modes:Zero Mode (ZM), Classical Mode (CM) and Revise bit order Mode (RM). At the encoder, according to the block mode information, the quantified coefficients are converted to their binary representations with different methods. At the decoder, according to the block mode information, different bits belonging to different block mode in one bitplane will have different likelihood-ratio calculation methods. And the reconstruction unit also utilizes the block mode information to realize the frame reconstruction. Simulation results show that our proposed decoding scheme could achieve better performance than the traditional decoding algorithm.
3. In the DVC, propose a rate estimation method at the decoder. In order to reduce the feedback times and the computing complexity at the decoder, we propose a new decoder rate estimation method. By combining the error estimation code used in the modulation mode selection with the Low-Density Parity-Check (LDPC) matrix in the DVC, this method makes use of the parity bits to estimate the bit error ratio(BER) of the bitplane without increasing any redundant bits. Finally, with the BER the minimum rate of the bitplane is calculated with the conditional entropy. Simulation results indicate that our proposed rate estimation method could save70%decoding time at the maximum.
4. In the DVC, according to the importance of data in the video frame, design an LDPC matrix with unequal error protection (UEP) function. In the irregular LDPC code, according to the wave effect in the belief propagation decoding process, the variable nodes having larger degree could get more reference information from the check node connected to it and could be decoded quickly and accurately. Thus, in this paper, we utilize the Progressive Edge-Growth (PEG) algorithm to generate an Weight-Increasing Parity-Check (WIPC) matrix as the LDPC matrix。Simultaneously, the decoder exploit the same method to partition all of blocks in the frame into three mode:Fast Motion (FM)、Moderate Motion (MM) and Stationary/Slow Motion (SM). The encoder will map the bits belonging to different modes to variable nodes with different degrees in the LDPC matrix for UEP. If the pixels located in the block belonging to FM, the corresponding bits of the pixels will be mapped to the variable nodes with the larger degrees/weights in the WIPC matrix; if the pixels located in the block belonging to SM, the corresponding binary representations of the pixels will be mapped to the variable nodes with the smaller weights; otherwise, the remaining binary representations of the pixels will be mapped to the other variable nodes. With this method, we can realize the UEP and obtain better compression efficiency and decoding performance.
5. In the DCVS, according to the sparsity of the video signal in the transform domain, design a Low-Density Sensing matrix (LDSM) with UEP function. In this paper, we extend the design thought about LDPC matrix with UEP function in DVC to the DCVS and design an LDSM with UEP function. In this paper, we also adopt the PEG algorithm to generate an WIPC matrix as the LDSM. Then the coefficients after discrete cosine transform will be mapped to the variable nodes with different degree in the LDSM. The larger coefficients will correspond to the variable nodes with larger degree and the smaller coefficients will correspond to the variable nodes with smaller degree. With this method, the larger coefficients could be effectively protected and have better recovery quality at low signal to noise ratios.
6. Design a new modulation method with UEP function for DVC Taken a QAM constellation map with Gray code as an example, according to the calculating results, different bit position of the symbol in the constellation map have different priority. Therefore, if we could place the parity bits of more important bitplanes in more protection bit position of the constellation map and place the parity bits of less important bitplanes in less protection bit position, we will realize the UEP of the data bits without consuming any additional resources. The simulation results show that compared with the traditional method in the DVC, PSNR gain for our proposed scheme can reach up to1db.
引文
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