基于边信息的分布式视频压缩感知重建

WZ视频编码系统中的虚拟信道会因信道模型参数估计的不稳定导致系统性能下降,为了克服该问题,提出不带有虚拟信道的分布式视频压缩感知重建算法,仅靠解码端获得的压缩感知观测值对非关键帧的错误进行纠正,并利用平滑投影Landweber迭代求解联合重建模型.实验证明本文所提出算法获得了更好的率失真性能和更低的重建时间.     

一种面向视频应用的有效实时码率控制算法

为提供实时视频传输服务,同时保证一定的视频质量,提出了一种基于JVT-G012的动态码率控制算法。在编码过程中,根据图像内容的运动特性,动态决定GOP长度,同时防止输出缓冲器的上溢和下溢;为复杂度较高的宏块（MB）分配较多的比特。实验结果表明,该算法对于场景变化较大的实际视频序列有较好的编码效果。     

基于自适应采样的多假设预测残差重构算法研究

为保证遥感视频序列的高质量重构,本文结合视频序列的高时空冗余特点,在基于块的分布式视频压缩感知（Distributed video compressed sensing,DVCS）框架的基础上提出了一种基于自适应采样的多假设预测残差重构模型及基于变采样率的多假设预测残差重构算法.首先对目标帧进行预测,根据各块预测精度的不同自适应地分配采样率;然后用变采样率多假设预测残差重构算法重构出目标帧;最后利用双向运动估计对重构结果进行修正.仿真结果表明该算法能够在降低采样率的同时保证良好的主客观重构质量;相同采样率条件下,重构精度比MC-BCS-SPL算法提高大约7dB,比MH-BCS-SPL算法提高大约1dB.     

Compressed-sensing recovery of multiview image and video sequences using signal prediction

In the compressed sensing of multiview images and video sequences, signal prediction is incorporated into the reconstruction process in order to exploit the high degree of interview and temporal correlation common to multiview scenarios. Instead of recovering each individual frame independently, neighboring frames in both the view and temporal directions are used to calculate a prediction of a target frame, and the difference is used to drive a residual-based compressed-sensing reconstruction. The proposed approach demonstrates a significant gain in reconstruction quality relative to the straightforward compressed-sensing recovery of each frame independently of the others in the multiview set, as well as a significant performance advantage as compared to a pair of benchmark multiple-frame compressed-sensing reconstructions.     

一种残差-预测重构的视频分布式压缩感知实现方法研究

为了降低计算成本并节约系统功耗,信号处理最新出现的理论-分布式压缩感知(Distributed Compressed Sensing,DCS)成为视频技术的应用焦点。为此,一种基于多假设预测的视频DCS(VDCS)方案被提出。在VDCS的解码端,当前帧的预测来自于以前重构的参考帧(CS帧),而残差作为重构条件用于改善视频的重构质量。实验结果表明,提出的残差-预测VDCS方法重构视频信号的峰值信噪比(PSNR)优于MH-BCS-SPL和传统的JSM-DCS处理方法。     

基于运动估计与回溯自适应正交匹配追踪的视频压缩传感

为了解决传统视频压缩传感方法中对视频逐帧单独重构所产生的图像模糊,将压缩传感理论与MPEG标准视频编码的相关技术相结合,提出了一种基于运动估计与运动补偿的视频压缩传感方法,以消除视频信号在空域和时域上的冗余。该方法在充分考虑视频序列时域相关性的同时,首先对视频图像进行前、后向和双向预测和补偿,然后采用回溯自适应正交匹配追踪(BAOMP)算法,对运动预测残差进行重构,最后实现当前帧的重构。实验结果表明,该方法较逐帧重构的视频图像质量有较大改善,且可获得更高的峰值信噪比。     

Low-complexity and high-quality frame-skipping transcoder for continuous presence multipoint video conferencing

This paper presents a new frame-skipping transcoding approach for video combiners in multipoint video conferencing. Transcoding is regarded as a process of converting a previously compressed video bitstream into a lower bitrate bitstream. A high transcoding ratio may result in an unacceptable picture quality when the incoming video bitstream is transcoded with the full frame rate. Frame skipping is often used as an efficient scheme to allocate more bits to representative frames, so that an acceptable quality for each frame can be maintained. However, the skipped frame must be decompressed completely, and should act as the reference frame to the nonskipped frame for reconstruction. The newly quantized DCT coefficients of prediction error need to be recomputed for the nonskipped frame with reference to the previous nonskipped frame; this can create an undesirable complexity in the real time application as well as introduce re-encoding error. A new frame-skipping transcoding architecture for improved picture quality and reduced complexity is proposed. The proposed architecture is mainly performed on the discrete cosine transform (DCT) domain to achieve a low complexity transcoder. It is observed that the re-encoding error is avoided at the frame-skipping transcoder when the strategy of direct summation of DCT coefficients is employed. By using the proposed frame-skipping transcoder and dynamically allocating more frames to the active participants in video combining, we are able to make more uniform peak signal-to-noise ratio (PSNR) performance of the subsequences and the video qualities of the active subsequences can be improved significantly.     

Recovery of image and video based on compressive sensing via tensor approximation and Spatio-temporal correlation

In recent years, block-based compressive sensing (BCS) has been extensively studied because it can reduce computational complexity and data storage by dividing the image into smaller patches, but the performance of the reconstruction algorithm is not satisfactory. In this paper, a new reconstruction model for image and video is proposed. The model makes full use of spatio-temporal correlation and utilizes low-rank tensor approximation to improve the quality of the reconstructed image and video. For image recovery, the proposed model obtains a low-rank approximation of a tensor formed by non-local similar patches, and improves the reconstruction quality from a spatial perspective by combining non-local similarity and low-rank property. For video recovery, the reconstruction process is divided into two phases. In the first phase, each frame of the video sequence is regarded as an independent image to be reconstructed by taking advantage of spatial property. The second phase performs tensor approximation through searching similar patches within frames near the target frame, to achieve reconstruction by putting the spatio-temporal correlation into full play. The resulting model is solved by an efficient Alternating Direction Method of Multipliers (ADMM) algorithm. A series of experiments show that the quality of the proposed model is comparable to the current state-of-the-art recovery methods.

     

基于压缩域的关键帧快速提取方法

关键帧提取技术是基于内容检索和视频分析的基础。关键帧的使用减少了视频索引的数据量,同时也为视频摘要和检索提供了一个组织框架。首先介绍了目前的关键帧提取技术,然后提出了一种基于运动特征利用模糊推理算法从MPEG视频流中提取关键帧的方法。由于处理过程是直接从MPEG的压缩视频提取,不需对其解压,所以计算复杂度低,提高了提取速度。实验证明该方法效率高,可以比较好地代表视频内容。     

基于自适应码率分配的压缩传感深度视频编码方法

压缩传感深度视频(CSDV)是由深度视频经过压缩得到,它的冗余信息仍然巨大,由此,文中提出基于高斯混合模型和边缘码率分配的深度视频编码方法.在时域方向上,使用压缩传感,压缩八帧深度视频,得到一帧CSDV图像.为了减小量化的计算复杂度,将一帧CSDV图像分割成一系列大小相同且互不重合的视频块,使用Canny算子作为边界提取工具提取视频块的边界.根据每个视频块中非零像素所占的百分比,给不同的视频块分配不同的比特数.在模型中,使用高斯混合模型建模这些视频块,用于设计乘积矢量量化器,再使用乘积矢量量化器量化这些视频块.     

基于MPEG-2压缩域的非线性编辑特技算法

在数字电视节目的制作过程中,非线性编辑中视频特技合成是一项很重要的工作。目前,这些特技大都是在宅间域中实现的,但是由于数字视频的压缩编码大都采用MPEG-2标准,因此直接在MPEG-2压缩域实现视频特技的合成,可以减少很多中间步骤,从阿加快电视台基于MPEG-2的数字电视节目的制作播出一体化进程。该文提出了一种在MPEG-2压缩域中不完全解压缩蛮现镜像特技的算法。实验结果表明,采用这种算法后,计算量比传统的空间域实现特技有了很大的减少,而视频图像的质量损失很小。     

Genuine reversible data hiding technology using compensation for H.264 bitstreams

Reversible data hiding technologies have been considered largely impractical because those are, in most cases, applicable to raw video data rather than prevailing compressed data. Even though, many algorithms have been recently developed in the compressed video domain, most of them cannot guarantee the reversibility of cover video due to the lossy characteristics of video compression standards. We suggest completely practical data hiding scheme for H.264 baseline bitstream by achieving genuine reversibility for both I and P frames. Regardless of the data hiding algorithm, the proposed scheme can increase embedding payload by 66.9% and reduce computational complexity by 93%. Also, a novel compensation based difference expansion method with clever coefficient pairing strategy is proposed as a data hiding algorithm and achieved superior embedding payload vs. image quality performance. The proposed algorithm improves payload by 48.9% on average at almost the same video quality distortion.     

基于时空相关性的分布式压缩感知多假设预测重构算法

综合考虑到视频序列本身的不同特性以及时空相关性, 将传统视频编码中的多假设预测运动估计思想引入到分布式压缩感知视频编码系统中, 提出一种新的基于时空相关性的分布式压缩感知多假设预测重构算法。在编码端增加CS帧的块模式判别, 在解码端CS帧根据模式判别进行相应的基于块的多假设预测估计和残差稀疏重构。仿真实验表明, 与传统的分布式压缩感知多假设预测重构算法相比, 所提出的算法能够较好地改善视频序列的重建质量。     

基于矢量直方图迁移的视频加密域可逆隐写方案

针对压缩域视频隐藏算法嵌入容量低、不可见性差的问题,提出了一种H.264/AVC加密域的可逆隐写方案。首先由嵌入容量和载体大小决定参考帧间隔参数,并根据需要决定是否对载体进行加密;然后,根据待嵌视频帧数生成嵌入密钥;最后通过压缩视频中矢量直方图迁移,实现运动矢量上的可逆信息嵌入。所提方案通过指定解码参考帧,克服了由于运动矢量修改而造成的失真累加效应。所提方案兼容基于运动矢量的视频加密算法,视频的解密和信息提取分别依赖解密密钥和嵌入密钥,两者之间相互分离,在视频密文域或者解密后的明文域中均能提取信息并无损恢复视频载体。信息的安全性依赖于嵌入密钥,密钥长度可以根据需要控制,最大长度等于可嵌入信息的帧数。实验表明该方案计算复杂度低,安全度高,并可以根据嵌入负载调整容量和不可见性,与BCH码可逆嵌入方案相比PSNR值提高3~5 dB,平均嵌入容量增加5~10倍。     

基于卷积神经网络的压缩感知重构算法优化

压缩感知理论因其编码复杂度低、节省资源、抗干扰能力强等特点,被广泛应用于图像和视频信号处理。然而,传统的压缩感知技术也面临着重构时间长、算法复杂度高、迭代次数多、计算量大等问题。针对图像重构时间和重构质量的问题,文中提出一种新的卷积神经网络结构Combine Network(CombNet),它将压缩感知的测量值作为卷积神经网络的输入,连接一个全连接层,然后通过CombNet获得最终输出。实验结果表明,CombNet具有较低的复杂度及较好的恢复性能,在相同的采样率下,CombNet的峰值信噪比(PSNR)较TVAL3提高了7.2%~13.95%,较D-AMP提高了7.72%~174.84%。CombNet重构的耗时比传统重构算法提高了3个数量级,实现了实时重构。在采样率极低(采样率为0.01时)的情况下,CombNet的平均PSNR较D-AMP高出11.982 dB,因此所提算法具有更好的视觉吸引力。     

基于半像素运动估计的DCVS残差重建算法

针对分布式视频压缩感知中非关键帧重建质量较差的问题进行了研究,提出了一种基于半像素插值技术的残差多假设块迭代重建算法。该算法先对重建后的相邻关键帧进行半像素插值运动估计生成当前非关键帧的边信息;然后,在测量域内对非关键帧与边信息求残差并对残差进行多假设重建;最后,将重建的残差与边信息进行融合得到重建的非关键帧。仿真结果表明,基于半像素插值的改进残差多假设块迭代重建算法比多假设迭代重建算法重建质量平均提高了0.3~1.4dB,提高了非关键帧的重建质量。     

保留边缘与细节的压缩采样视频复原算法*

已有的压缩感知视频复原算法因过平滑效应难以保留视频帧的边缘与细节信息,对此提出一种基于混合稀疏性测量的压缩采样视频复原算法。编码端将视频序列分为关键帧与非关键帧,并使用相同的感知矩阵对帧的每块进行采样。解码端则设计了考虑局部稀疏性与全局稀疏性的混合稀疏性测量方案,并将其作为压缩感知视频复原问题的正则项;然后,通过分裂Bregman迭代算法对关键帧进行解码,并考虑视频帧间的时间相关性对非关键帧进行细化处理。基于多组仿真实验的结果表明,本算法获得了较好的视频复原精度,并具有理想的计算时间性能。     

基于自适应参考帧模式选择的多视点视频编码方法

基于多视点视频序列相关性,提出独立片的概念,进而提出一种采用自适应参考帧模式选择的多视点视频编码方法.在保证高率失真性能的前提下,应用独立片、自适应算法有效地降低了多视点视频编码计算复杂度,将多种性能进行有机结合,可以根据用户外部设置的参数调整自适应模式.实验结果表明,该方法在保证高压缩效率的前提下,能有效地降低计算复杂度,并改善随机访问性能.     

基于失真估计和码率控制的视频传输新方案

针对差错信道上的实时视频编码和传输问题,提出一种基于逐帧失真估计和码率控制的新型时域不等差错保护方案。该方案给出一种低复杂度逐帧传输失真估计模型,估计差错信道下的视频传输失真,结合码率控制算法,通过对编码量化参数的简单搜索,逐帧求出最优的压缩编码和信道保护策略。仿真结果表明,与现有方案相比,该方案不但延迟小、复杂度适中,而且能提高重建视频序列的平均质量,减少逐帧之间的质量波动。     

一种基于MPEG-4的帧间差分能量视频水印算法

针对视频水印的盲检测特性和对时间轴上帧操作的鲁棒性,论文提出了一种基于相邻帧间相关性的视频水印算法,即在预测帧的编码过程中,根据编码器的运动估计和补偿模块提供的运动向量和宏块的编码模式,在残差中嵌入水印数据。在该算法中,用能量替换函数保证嵌入数据不影响视频质量,用重同步策略来保证水印提取时的鲁棒性,并且在水印提取的过程中不需要任何附加信息,是一种盲检测算法。水印的嵌入和提取是在视频的编、解码过程中直接利用编码器提供的信息进行的,因此该算法具有计算复杂性低的特点。实验证明这种方法能够抵抗重编码操作,并且对于帧删除、帧插入和帧置换等攻击也有一定的鲁棒性,而且由于该算法是在预测帧的残差中进行,水印的隐藏效果比较好,生成码流的信噪比也较高。