分布式信源编码关键技术研究
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摘要
传统的图像信源编码算法如视频编码标准MPEG-X和H.26X或静止图像编码标准JPEG2000等，都是在编码端提取信源间的统计相关性来进行压缩编码，编码端的运算复杂度都高于解码端。随着电子技术的发展，一些新兴的应用如无线视频传感网络、相机阵列等得到了快速的发展。这些新的应用由于编码端资源有限、功耗有限，不太适合采用传统的图像信源编码算法，并对传统的图像编码算法以及系统架构提出了新的挑战。
近年来，分布式信源编码（Distributed Source Coding, DSC）由于其编码端复杂度低、良好的率失真性能和较好的抗误码性能受到了学者的广泛关注。在20世纪70年代Slepian和Wolf首先提出了DSC概念，他们利用信息熵提出针对于两个信息互相关联信源无损压缩的理论极限，并证明了两个互不通信的信息相关的信源压缩可以达到有互相通信的压缩效率。随后Wyner和Ziv将其扩展到了有损压缩，在考虑高斯信源有损压缩时得到了类似的结论。Slepian-Wolf和Wyner-Ziv所提出的编码理论与方法奠定了DSC的理论基础。与传统的图像编码算法不同，分布式信源编码将相关性的提取工作从编码端转移到了解码端，编码端运算复杂度大大降低。DSC由于其独特的优势，已成为国内外的研究热点。本文对DSC的几个关键技术进行了深入的研究并提出了相应的解决方案。
本文的主要工作及取得的主要研究成果为：
1.研究了分布式信源编码的理论，对基于Turbo码和LDPC码两种分布式信源编码系统进行了研究，提出了一种基于变换域的分布式信源编码系统架构。该系统在编码端首先进行DCT变换，然后按子带将量化后的比特平面数据进行LDPCA编码。在解码端利用关键帧重建边信息，使用边信息进行LDPCA解码，最后重构原始图像。该方案利用了传统编码框架的DCT变换和量化技术，可以获得比传统帧内编码算法更好的压缩性能。
2.针对反馈信道的存在导致了时延的增加和解码器复杂度提高的缺点，提出了一种无反馈式分布式信源编码方案。该方案在编码端使用快速运动估计算法来重建边信息帧，并使用拉普拉斯分布模型来估计码率。由于去掉了反馈信道，系统的解码端复杂度大大降低，率失真性能损失也控制在0.3dB以内，适用于反馈信道不存在或者对系统时延要求较高的应用场景。
3.针对分布式视频编码（DVC）中的边信息重建问题，提出了一种空域平滑的边信息重建算法。该算法设置代价函数阈值保证目标运动轨迹的线性连续，提高了运动估计准确性。对于运动比较平稳的区域，采用双向运动估计和补偿插值生成边信息；对于运动剧烈区域，使用最大后验概率估计对错误插值块进行空域平滑，最后利用最小均方误差准则最优重构。实验表明，该算法降低了编码码率，同时提高了重建图像PSNR。
4.基于超光谱图像特点和应用环境要求，提出一种基于DCT变换域DSC的超光谱图像压缩算法。在编码端使用部分像素点进行线性预测，从而减少了编码端的运算量；在解码端，利用已解码子带信息进行基于块的迭代线性预测，使用优化后的边信息解码后续的子带。与传统超光谱图像压缩算法相比，本算法在编码端的运算量更少，需要的存储空间更小，满足超光谱图像压缩系统要求，易于硬件实现。
5.基于现有的Slepian-Wolf解码器解码复杂度过高的问题，提出并实现了基于CUDA并行计算技术的快速分布式信源解码方法。使用GPU（GraphicProcessing Unit）对DSC中解码模块进行了并行计算优化，LDPCA解码器加速比可达200倍。与传统的基于CPU的解码方法相比，QCIF解码加速比为10倍，CIF加速比可达20倍。使用该并行计算技术提高了DSC的解码速度，有重要的应用价值。
对于上述提出的算法，论文通过大量的软件仿真、测试以及与其他算法的比较验证了其有效性和先进性。
Traditional image source coding algorithms, such as video coding standardsMPEG-X and H.26X or still image coding standard JPEG2000, extract the statisticalcorrelation at the encoder. Computational complexity of the encoder is usually muchhigher than the decoder. With the development of electronic technology, a number ofemerging applications such as wireless video sensor network and camera array havebeen widely used. These new applications have constrained resources and powerconsumption at the encoder. It’s not very suitable to apply the traditional image sourcecoding algorithm in these applications. This is a new challenge to traditional imagecoding algorithms and system architectures.
In recent years, Distributed Source Coding (DSC) is being researched by more andmore scholars due to its low encoding complexity, good performance and better errorresilience robustness. Distributed source coding concept was first proposed by Slepianand Wolf in the1970s for lossless compression. They proved that two independentcorrelated sources can achieve the same compression efficiency compared withcompress jointly. Wyner and Ziv then extended it to lossy compression and got similarresults. Slepian-Wolf and Wyner-Ziv two theories laid the theoretical basis of theDistributed source coding. The distributed source coding extract statistical correlation atthe decoder which transfers the complexity from the encoder to the decoder comparedwith traditional image source coding algorithms. Distributed source coding has nowbecome a new research hotspot in worldwide. In this paper, several key technologies inDistributed Source Coding are researched and appropriate solutions are proposed.
The main work and research results obtained include:
1. A transform-domain distributed source coding architecture is proposed based onresearch of Turbo-based and LDPC-based system structures. At encoder, DCT transformis performed first. Then, bitplane data of subbands after DCT transform and quantitize isencoded with LDPCA. At decoder, side information reconstructed by key frames is usedfor LDPCA decoding. Finally encoded Wyner-Ziv frame is reconstructed with decodedbitplane data. With DCT transform and quantitize technology, this system can achievebetter performance compared with traditional intra-frame coding method.
2. A new Distributed Source Coding scheme without feedback channel is proposedto overcome the shortcomings of the feedback channel. The encoder uses fast motionestimation algorithm to reconstruct the side information, and use the Laplace distribution model to estimate the proper bit rate. Since the feedback channel is removed,the system complexity is greatly reduced. The rate distortion performance loss is controlin0.3dB or less and this scheme is suitable for those scenarios feedback channel doesnot exist or high system delay is unacceptable.
3. An improved side information interpolation algorithm is presented. We set thefunctional threshold to ensure the linear continuity of target motion track. For smoothmotion area, bi-direction motion estimation and compensate is adopted to generate theside information. For other area, side information macroblock is chosen from a series ofseveral candidate macroblocks with minimum edge match error. Optimal minimummean-error reconstruction algorithm is also adopted to enhance the reconstructionperformance. Experiments show our algorithm can achieve better PSNR performancewhile decreases the encoding rate.
4. Based on the analyses of the hyper-spectral images, a new compressionalgorithm based on DCT transform domain distributed source coding is proposed. Itperforms the bitplane encoding at the encoder with DCT subbands order, while usingthe key frame to reconstruct the side information for LDPC decoding at the decoder.Few pixels are adopted to perform linear prediction at encoder which reduces thecomplexity. Subbands which previously decoded are utilized for iterative linearprediction based on blocks at decoder, and following subbands are decoded withoptimized side information. The experimental results show that the proposed algorithmachieves improved performance over the conventional algorithm, and efficientlyreduces the cost of computation and memory usage at the encoder which facilitates thehardware implementation.
5. Channel coding based Slepian-Wolf decoding takes up more than90%of theentire decoding complexity of the Wyner-Ziv system. A high performance CUDAparallel computing of distributed source decoder is proposed. GPU basedimplementation obtains up to200x speedup for LDPCA decoding. Compared totraditional CPU-based implementation, GPU parallel computing optimization achieves aspeedup of10x for QCIF decoding, and speedup of20x for CIF decoding. With highperformance parallel computing technology Distributed Source Coding is becomingavailable for practical application.
For the above algorithms, a large number of simulations and experiments areperformed to verify their validity and advantages.
近年来，分布式信源编码（Distributed Source Coding, DSC）由于其编码端复杂度低、良好的率失真性能和较好的抗误码性能受到了学者的广泛关注。在20世纪70年代Slepian和Wolf首先提出了DSC概念，他们利用信息熵提出针对于两个信息互相关联信源无损压缩的理论极限，并证明了两个互不通信的信息相关的信源压缩可以达到有互相通信的压缩效率。随后Wyner和Ziv将其扩展到了有损压缩，在考虑高斯信源有损压缩时得到了类似的结论。Slepian-Wolf和Wyner-Ziv所提出的编码理论与方法奠定了DSC的理论基础。与传统的图像编码算法不同，分布式信源编码将相关性的提取工作从编码端转移到了解码端，编码端运算复杂度大大降低。DSC由于其独特的优势，已成为国内外的研究热点。本文对DSC的几个关键技术进行了深入的研究并提出了相应的解决方案。
本文的主要工作及取得的主要研究成果为：
1.研究了分布式信源编码的理论，对基于Turbo码和LDPC码两种分布式信源编码系统进行了研究，提出了一种基于变换域的分布式信源编码系统架构。该系统在编码端首先进行DCT变换，然后按子带将量化后的比特平面数据进行LDPCA编码。在解码端利用关键帧重建边信息，使用边信息进行LDPCA解码，最后重构原始图像。该方案利用了传统编码框架的DCT变换和量化技术，可以获得比传统帧内编码算法更好的压缩性能。
2.针对反馈信道的存在导致了时延的增加和解码器复杂度提高的缺点，提出了一种无反馈式分布式信源编码方案。该方案在编码端使用快速运动估计算法来重建边信息帧，并使用拉普拉斯分布模型来估计码率。由于去掉了反馈信道，系统的解码端复杂度大大降低，率失真性能损失也控制在0.3dB以内，适用于反馈信道不存在或者对系统时延要求较高的应用场景。
3.针对分布式视频编码（DVC）中的边信息重建问题，提出了一种空域平滑的边信息重建算法。该算法设置代价函数阈值保证目标运动轨迹的线性连续，提高了运动估计准确性。对于运动比较平稳的区域，采用双向运动估计和补偿插值生成边信息；对于运动剧烈区域，使用最大后验概率估计对错误插值块进行空域平滑，最后利用最小均方误差准则最优重构。实验表明，该算法降低了编码码率，同时提高了重建图像PSNR。
4.基于超光谱图像特点和应用环境要求，提出一种基于DCT变换域DSC的超光谱图像压缩算法。在编码端使用部分像素点进行线性预测，从而减少了编码端的运算量；在解码端，利用已解码子带信息进行基于块的迭代线性预测，使用优化后的边信息解码后续的子带。与传统超光谱图像压缩算法相比，本算法在编码端的运算量更少，需要的存储空间更小，满足超光谱图像压缩系统要求，易于硬件实现。
5.基于现有的Slepian-Wolf解码器解码复杂度过高的问题，提出并实现了基于CUDA并行计算技术的快速分布式信源解码方法。使用GPU（GraphicProcessing Unit）对DSC中解码模块进行了并行计算优化，LDPCA解码器加速比可达200倍。与传统的基于CPU的解码方法相比，QCIF解码加速比为10倍，CIF加速比可达20倍。使用该并行计算技术提高了DSC的解码速度，有重要的应用价值。
对于上述提出的算法，论文通过大量的软件仿真、测试以及与其他算法的比较验证了其有效性和先进性。
Traditional image source coding algorithms, such as video coding standardsMPEG-X and H.26X or still image coding standard JPEG2000, extract the statisticalcorrelation at the encoder. Computational complexity of the encoder is usually muchhigher than the decoder. With the development of electronic technology, a number ofemerging applications such as wireless video sensor network and camera array havebeen widely used. These new applications have constrained resources and powerconsumption at the encoder. It’s not very suitable to apply the traditional image sourcecoding algorithm in these applications. This is a new challenge to traditional imagecoding algorithms and system architectures.
In recent years, Distributed Source Coding (DSC) is being researched by more andmore scholars due to its low encoding complexity, good performance and better errorresilience robustness. Distributed source coding concept was first proposed by Slepianand Wolf in the1970s for lossless compression. They proved that two independentcorrelated sources can achieve the same compression efficiency compared withcompress jointly. Wyner and Ziv then extended it to lossy compression and got similarresults. Slepian-Wolf and Wyner-Ziv two theories laid the theoretical basis of theDistributed source coding. The distributed source coding extract statistical correlation atthe decoder which transfers the complexity from the encoder to the decoder comparedwith traditional image source coding algorithms. Distributed source coding has nowbecome a new research hotspot in worldwide. In this paper, several key technologies inDistributed Source Coding are researched and appropriate solutions are proposed.
The main work and research results obtained include:
1. A transform-domain distributed source coding architecture is proposed based onresearch of Turbo-based and LDPC-based system structures. At encoder, DCT transformis performed first. Then, bitplane data of subbands after DCT transform and quantitize isencoded with LDPCA. At decoder, side information reconstructed by key frames is usedfor LDPCA decoding. Finally encoded Wyner-Ziv frame is reconstructed with decodedbitplane data. With DCT transform and quantitize technology, this system can achievebetter performance compared with traditional intra-frame coding method.
2. A new Distributed Source Coding scheme without feedback channel is proposedto overcome the shortcomings of the feedback channel. The encoder uses fast motionestimation algorithm to reconstruct the side information, and use the Laplace distribution model to estimate the proper bit rate. Since the feedback channel is removed,the system complexity is greatly reduced. The rate distortion performance loss is controlin0.3dB or less and this scheme is suitable for those scenarios feedback channel doesnot exist or high system delay is unacceptable.
3. An improved side information interpolation algorithm is presented. We set thefunctional threshold to ensure the linear continuity of target motion track. For smoothmotion area, bi-direction motion estimation and compensate is adopted to generate theside information. For other area, side information macroblock is chosen from a series ofseveral candidate macroblocks with minimum edge match error. Optimal minimummean-error reconstruction algorithm is also adopted to enhance the reconstructionperformance. Experiments show our algorithm can achieve better PSNR performancewhile decreases the encoding rate.
4. Based on the analyses of the hyper-spectral images, a new compressionalgorithm based on DCT transform domain distributed source coding is proposed. Itperforms the bitplane encoding at the encoder with DCT subbands order, while usingthe key frame to reconstruct the side information for LDPC decoding at the decoder.Few pixels are adopted to perform linear prediction at encoder which reduces thecomplexity. Subbands which previously decoded are utilized for iterative linearprediction based on blocks at decoder, and following subbands are decoded withoptimized side information. The experimental results show that the proposed algorithmachieves improved performance over the conventional algorithm, and efficientlyreduces the cost of computation and memory usage at the encoder which facilitates thehardware implementation.
5. Channel coding based Slepian-Wolf decoding takes up more than90%of theentire decoding complexity of the Wyner-Ziv system. A high performance CUDAparallel computing of distributed source decoder is proposed. GPU basedimplementation obtains up to200x speedup for LDPCA decoding. Compared totraditional CPU-based implementation, GPU parallel computing optimization achieves aspeedup of10x for QCIF decoding, and speedup of20x for CIF decoding. With highperformance parallel computing technology Distributed Source Coding is becomingavailable for practical application.
For the above algorithms, a large number of simulations and experiments areperformed to verify their validity and advantages.
引文
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