Distributed compressed video sensing based on the optimization of hypothesis set update technique

Multimedia Tools and Applications - Compressed Sensing (CS) breakthroughs the limitation of Nyquist sampling rate and realizes the sampling and compression of data simultaneous. Hence, it is widely...     

An efficient compressed domain video indexing method

Video indexing is employed to represent the features of video sequences. Motion vectors derived from compressed video are preferred for video indexing because they can be accessed by partial decoding; thus, they are used extensively in various video analysis and indexing applications. In this study, we introduce an efficient compressed domain video indexing method and implement it on the H.264/AVC coded videos. The video retrieval experimental evaluations indicate that the video retrieval based on the proposed indexing method outperforms motion vector based video retrieval in 74 % of queries with little increase in computation time. Furthermore, we compared our method with a pixel level video indexing method which employs both temporal and spatial features. Experimental evaluation results indicate that our method outperforms the pixel level method both in performance and speed. Hence considering the speed and precision characteristics of indexing methods, the proposed method is an efficient indexing method which can be used in various video indexing and retrieval applications.     

An improved distributed compressed video sensing scheme in reconstruction algorithm

Multimedia Tools and Applications - Under the new video application scene of resource-constrained coding side such as wireless sensor networks, compressed sensing technique provides the possibility...     

视频压缩感知中分级多假设预测算法

目的 多假设预测是视频压缩感知多假设预测残差重构算法的关键技术之一,现有的视频压缩感知多假设预测算法中预测分块固定,这种方法存在两点不足：1）对于视频帧中运动形式复杂的图像块预测效果不佳;2）对于运动平缓区域,相邻图像块的运动矢量非常相近,每块单独通过运动估计寻找最佳匹配块,导致算法复杂度较大。针对这些问题,提出了分级多假设预测思路（Hi-MH）,即对运动复杂程度不同的区域采取不同的块匹配预测方法。方法 对于平缓运动区域的图像块,利用邻域图像块的运动矢量预测当前块的运动矢量,从而降低运动估计的算法复杂度;对于运动较复杂的图像块,用更小的块寻找最佳匹配;对于运动特别复杂的图像块利用自回归模型对单个像素点进行预测,提高预测精度。结果 Hi-MH算法与现有的快速搜索预测算法相比,每帧预测时间至少缩短了1.4 s,与现有最优的视频压缩感知重构算法相比,对于运动较为复杂的视频序列,峰值信噪比（PSNR）提升幅度达到1 dB。结论 Hi-MH算法对于运动形式简单的视频序列或区域降低了计算复杂度,对于运动形式较为复杂的视频序列或区域提高了预测精度。     

采用压缩传感的鲁棒的视频指纹方案

视频指纹技术在视频检索、识别、安全等领域有着广泛的应用,提出一种基于压缩传感理论的鲁棒性视频指纹方法,该方法采用压缩传感的稀疏性和安全性对提取的视频关键帧进行采样,再对采样矩阵分块与分类,提取能量值大的一些子块构成新的特征矩阵.对特征矩阵使用奇异值分解,对较大奇异值量化编码生成指纹.同时,也提出了高效的两步匹配方案,通过粗精两步搜索对视频进行检索,提高了视频搜索速度,实验结果表明,能准确检测视频片段,对通常的视频处理具有较强鲁棒性,满足视频检索的实时要求.     

An AK-BRP dictionary learning algorithm for video frame sparse representation in compressed sensing

Sparsifying transform is an important prerequisite in compressed sensing. And it is practically significant to research the fast and efficient signal sparse representation methods. In this paper, we propose an adaptive K-BRP (AK-BRP) dictionary learning algorithm. The bilateral random projection (BRP), a method of low rank approximation, is used to update the dictionary atoms. Furthermore, in the sparse coding stage, an adaptive sparsity constraint is utilized to obtain sparse representation coefficient and helps to improve the efficiency of the dictionary update stage further. Finally, for video frame sparse representation, our adaptive dictionary learning algorithm achieves better performance than K-SVD dictionary learning algorithm in terms of computation cost. And our method produces smaller reconstruction error as well.

     

Residual domain dictionary learning for compressed sensing video recovery

Multimedia Tools and Applications - For compressed sensing (CS) recovery, the reconstruction quality is highly dependent on the sparsity level of the representation for the signal. Motivated by the...     

A compressed sensing approach for query by example video retrieval

Recently, compressed Sensing (CS) has theoretically been proposed for more efficient signal compression and recovery. In this paper, the CS based algorithms are investigated for Query by Example Video Retrieval (QEVR) and a novel similarity measure approach is proposed. Combining CS theory with the traditional discrete cosine transform (DCT), better compression efficiency for spatially sparse is achieved. The similarity measure from three levels (frame level, shot level and video level, respectively) is also discussed. For several different kinds of natural videos, the experimental results demonstrate the effectiveness of system by the proposed method.     

Forward-backward pursuit method for distributed compressed sensing

In this paper, a forward-backward pursuit method for distributed compressed sensing (DCSFBP) is proposed. In contrast to existing distributed compressed sensing (DCS), it is an adaptive iterative approach where each iteration consists of consecutive forward selection and backward removal stages. And it not needs sparsity as prior knowledge and multiple indices are identified at each iteration for recovery. These make it a potential candidate for many practical applications, when the sparsity of signals is not available. Numerical experiments, including recovery of random sparse signals with different nonzero coefficient distributions in many scenarios, in addition to the recovery of sparse image and the real-life electrocardiography (ECG) data, are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing DCS algorithms.     

压缩视频感知中量化器的研究与设计

压缩视频感知（Compressed Video Sensing,CVS）是一种利用压缩感知（Compressed Sensing,CS）以及分布式视频编码（DVC）的视频压缩方法,故又被称为分布式视频压缩感知。在CVS中,每帧图像经过块划分、压缩采样后对数据进行DPCM,最后使用均匀或者非均匀量化进行量化。目前,CVS量化器的设计大多是在采样数据或残差数据服从高斯分布的前提下设计的,通过Kolmogorov-Smirnov检验进一步分析压缩采样后的数据,利用劳埃德最佳量化器准则训练量化码书,设计出一种简单、高效的量化器。经实验,设计的量化器相比于传统的量化方法在BD-Rate上减少了约14.2%,在BDPSNR上提升了约0.11?dB,提高了CVS的压缩效率和重建质量。     

Backtracking-based matching pursuit method for distributed compressed sensing

In this paper, we study a distributed compressed sensing (DCS) problem in which we need to recover a set of jointly sparse vectors from the measurements. A Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) method to approximately sparse solutions for DCS is proposed. It is an iterative approach where each iteration consists of consecutive forward selection to adaptively choose several atoms and backward removal stages to detect the previous chosen atoms’ reliability and then delete the unreliable atoms at each iteration. Also, unlike its several predecessors, the proposed method does not require the sparsity level to be known as a prior which makes it a potential candidate for many practical applications, when the sparsity of signals is not available. We demonstrate the reconstruction ability of the proposed algorithm on both synthetically generated data and image using Normal and Binary sparse signals, and the real-life electrocardiography (ECG) data, where the proposed method yields less reconstruction error and higher exact recovery rate than other existing DCS algorithms.     

Adaptive residual-based distributed compressed sensing for soft video multicasting over wireless networks

Recently, multicasting of video signals has become a useful technology in wireless networks, in which the main challenge is to scalably serve multiple receivers that have different channel characteristics. In this paper, we propose an adaptive residual-based distributed compressed-sensing scheme for soft video multicast (ARDCS-cast). At the encoder, we first adaptively determine if a block in a non-reference frame should be measured directly or predictively during compressed-sensing. The resulting adaptive measurements from non-reference frames are then packeted together with the measurements of the reference frames. We further derive the optimal power allocation scheme for the measurements from each frame within each packet. The packets are then transmitted over the wireless channel. At the decoder, the receivers with different channel characteristics obtain different numbers of packets and reconstruct videos with different quality. Experimental results show that the proposed ARDCS-cast is more effective than the state-of-the-art SoftCast-2D, SoftCast-3D and DCS-cast schemes in both unicast and multicast scenarios.     

基于压缩感知的改进MPEG-2编码方案

为寻找压缩感知在视频编码上的应用并提高MPEG-2的编码效率,提出了基于压缩感知和MPEG-2的改进方案。该视频编码改进方案从标准重构方法与像素域最小全变分重构算法中选出最终重构方法,使最终重构出的图像具有较小均方误差和。像素域最小全变分重构算法的提出,基于原始图像的梯度比残差图像的梯度更稀疏这个特征。实验结果表明,所提出的方案对于各类序列都有性能的提升。对于有比较锐利边缘物体的序列,平均峰值信噪比(PSNR)提高0.5dB以上;而对于具有较多平坦区域或复杂纹理的序列,平均PSNR也有0.26dB~0.41dB的提高。     

基于变采样率压缩感知的视频压缩研究

业界分块视频压缩感知通常对所有图像块均采用相同的测量矩阵进行测量,这种方式未考虑到视频中不同区域的变化程度不同的事实。在视频帧间相关性的基础上提出一种自适应分配采样率的方法,即在编码端根据图像块的帧间相关性大小分类并分配不同的采样率;在解码端使用全变差算法以充分利用帧间相关性。为减小网络环境影响,此算法不区分参考帧与非参考帧,并对每一帧作相同处理。实验结果表明,该方法能够在较低采样率下重构出较高质量的视频图像,并且缩短计算时间。     

基于DCS的WMSN多视角视频编解码

为了有效解决无线多媒体传感器网络中多视角视频监控传输数据量大以及网络能量、资源受限的问题,提出了一种基于分布式压缩感知的高压缩率多视角视频编解码方法.对多视角视频序列进行分组处理,并将图像组分为关键帧和非关键帧;对关键帧采用基于压缩感知(compressed sensing,CS)的编解码方法进行处理;而在非关键帧的编码端采用联合稀疏表示方法对残差图像稀疏表示,解码端利用帧间时间相关性和多视角空间相关性预测生成当前视频帧,并借助差异补偿方法进一步提高预测准确性,同时提高了重构效果.实验结果表明,该方法取得较高的压缩率,重构出的图像质量比参考方法更高,且PSNR值得到了较大的提高.     

Distributed compressed video sensing based on key frame secondary reconstruction

Distributed compressed video sensing scheme combines advantages of compressive sensing and distributed video coding to get better performance, in the meantime, adapts to the limited-resource wireless multimedia sensor network. However, in the conventional distributed compressed video sensing schemes, self-similarity and high sampling rate of the key frame have not been sufficiently utilized, and the overall computational complexity increases with the development of these schemes. To solve the aforementioned problems, we propose a novel distributed compressed video sensing scheme. A new key frame secondary reconstruction scheme is proposed, which further improves the quality of key frame and decreases computational complexity. The key frame’s initial reconstruction value is deeply exploited to assist the key frame secondary reconstruction. Then, a hypotheses set acquisition algorithm based on motion estimation is proposed to improve the quality of hypotheses set by optimizing the searching window under low complexity. Experimental results demonstrate that the overall performance of the proposed scheme outperforms that of the state-of-the-art methods.     

Efficient compressed sensing based object detection system for video surveillance application in WMSN

Limited memory, energy and bandwidth are the major constraints in wireless visual sensor network (WVSN). Video surveillance applications in WVSN attracts a lot of attention in recent years which requires high detection accuracy and increased network lifetime that can be achieved by reducing the energy consumption in the sensor nodes. Compressed sensing (CS) based background subtraction plays a significant role in video surveillance application for detecting the presence of anomaly with reduced complexity and energy. This paper presents a system based on CS for single and multi object detection that can detect the presence of an anomaly with higher detection accuracy and minimum energy. A novel and efficient mean measurement differencing approach with adaptive threshold strategy is proposed for detection of the objects with less number of measurements, thereby reducing transmission energy. The performance of the system is evaluated in terms of detection accuracy, transmission energy and network lifetime. Furthermore, the proposed approach is compared with the conventional background subtraction approach. The simulation results show that the proposed approach yields better detection accuracy with 90% reduction in samples compared to conventional approach.     

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。