运动补偿预测残差稀疏重构的分布式压缩视频传感

针对低复杂度视频编码需求,基于压缩传感(CS：Compressive Sensing)理论,提出了一种分布式压缩视频传感算法。低复杂度的编码器独立随机投影关键帧和CS帧,采集压缩视频数据;在解码端进行运动补偿预测以利用帧间相关性,对预测残差稀疏重构实现CS帧重建。仿真测试表明,与现有的三种压缩视频传感算法相比,所提算法重建的视频质量更好,适合无线视频监控及无线视频传感网络等应用。     

Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation model

The existing video compressed sensing (CS) algorithms for inconsistent sampling ignore the joint correlations of video signals in space and time, and their reconstruction quality and speed need further improvement. To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains. Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction. Experimental results show that, compared with the existing algorithms, the proposed algorithm achieves a peak signal-to-noise ratio gain of 1–3 dB and a multi-scale structural similarity gain of 0.01–0.03 at a low time complexity, and the reconstructed frames not only have good edge contours but also retain textural details.     

运动补偿预测残差稀疏重构的压缩视频传感

针对低复杂度视频编码需求,基于压缩传感(Compressive Sensing,CS)理论,提出了一种分布式压缩视频传感算法。低复杂度的编码器独立随机投影关键帧和CS帧,采集压缩视频数据;在解码端进行运动补偿预测以利用帧间相关性,对预测残差稀疏重构实现CS帧重建。仿真测试表明,与现有的3种压缩视频传感算法相比,所提算法重建的视频质量更好,适合无线视频监控及无线视频传感网络等应用。     

分布式压缩感知视频编码中CS帧的二次修正准则研究

分布式视频编码是新的视频编码体系,与传统的视频编码体系相比,具有编码端相对简单、解码端相对复杂的特点。此外,压缩感知突破了奈奎斯特采样定理,降低了信号的采样率。将压缩感知理论应与分布式视频编码相结合,使编码端复杂度降低。在一些分布式压缩视频编码研究中,CS帧是由边信息和发送端传送的信息联合重建的,由于不同CS帧的边信息的预测准确度不同,导致不同CS帧恢复质量相差较大。为了解决这个问题,本文对CS帧的二次修正准则的进行研究,首先从理论上推导出方差作为修正准则的可行性,并在实验中加以验证。实验可知,本文提出的方法在一定程度上改善了这些帧的重建质量。      

基于边信息的分布式视频压缩感知的残差重构

压缩感知(Compressed Sensing,CS)结合了视频信号的变换和信息压缩过程,为简化编码算法提供了一种新的解决思路.把分布式视频编码(DVC)和CS结合在一起,构建简单的视频编码框架,并利用原始视频帧与边信息之间的相关性进行残差重构,提出了一种基于边信息的分布式视频压缩感知编解码方案.此方法对关键帧采用传统的帧内编、解码;对非关键帧CS进行随机观测提取观测向量,解码端利用优化的边信息和传输的CS观测向量进行联合重构.实验结果表明,该方法在运动较平滑的序列中比参考方案的恢复质量提高了4 ～6 dB.     

Compressive Sensing Based Soft Video Broadcast Using Spatial and Temporal Sparsity

Video broadcasting over wireless network has become a very popular application. However, the conventional digital video broadcasting framework can hardly accommodate heterogeneous users with diverse channel conditions, which is called the cliff effects. To overcome this cliff effects and provide a graceful degradation to multi-receivers, in this paper, we use the nonlocal sparsity and hierarchical GOP structure to propose a novel CS based soft video broadcast scheme. CS has properties of minimizing bandwidth consumption and generating measurements with equal importance which are exactly needed by video soft broadcast. In the proposed scheme, the measurement data are generated by block-wise compressive sensing (BCS), and then the measurement data packets are sent over a highly dense constellation though OFDM channel to achieve a simple encoder. Ideally, with the GOP structure, inter frame has lower sampling rate than intra frame to achieve better compression efficiency. At the decoder side, due to equally-important packets and property of soft broadcast, each user can receive the noise-corrupted measurements matching its channel condition and reconstruct video. The hierarchical GOP structure is presented to explode the correlation and non-local sparsity among video frames during the recover process. Additionally, using non-local sparsity, group based CS reconstruction with adaptive dictionaries is proposed to improve decoding quality. The experimental results show that the proposed scheme provides better performance compared with the traditional SoftCast with up to 8 dB coding gain for some channel conditions.     

视频压缩感知中基于结构相似的帧间组稀疏表示重构算法研究

基于视频帧内图像的非局部相似性和帧间信号的相关性,本文提出了一种基于结构相似的帧间组稀疏表示重构算法（SSIM-InterF-GSR）,有效地提高了视频压缩感知的重构性能.在SSIM-InterF-GSR算法中,提出以结构相似度（SSIM）作为相似块匹配准则,在当前帧和参考帧内搜索匹配块生成相似块组,以相似块组的稀疏性作为正则项重构当前帧.同时,还提出了阶梯递减匹配块个数调整方案用于SSIM-InterF-GSR重构算法的迭代过程.仿真结果表明,相比于目前最好的视频压缩感知重构算法（Up-Se-AWEN-HHP）,本文算法获得了更好的重构质量,最多可提升4~5dB.     

Video compressive sensing reconstruction via long- short-term double-pattern prediction

The compressive sensing technology has a great potential in high-dimensional vision processing. The existing video reconstruction methods utilize the multihypothesis prediction to derive the residual sparse model from key frames. However, these methods cannot fully utilize the temporal correlation among multiple frames. Therefore, this paper proposes the video compressive sensing reconstruction via long-short-term double-pattern prediction, which consists of four main phases:the first phase reconstructs each frame independently; the second phase adaptively updates multiple reference frames; the third phase selects the hypothesis matching patches from current reference frames; the fourth phase obtains the reconstruction results by using the patches to build the residual sparse model. The experimental results demonstrate that as compared with the state-of-the-art methods, the proposed methods can obtain better prediction accuracy and reconstruction quality for video compressive sensing.     

一种基于CS的LDPC码的抗误码方法

提出了一种基于CS(压缩感知)的LDPC码(低密度奇偶校验码)的抗误码方法。在编码端对H.264视频流中的每一个NAL(网络提取层)进行基于IEEE802.16e的LDPC编码。在解码端进行两种方法的解码,一种进行传统的LDPC解码,另一种根据压缩感知线性解码和信道码线性解码之间的联系,将基于IEEE802.16e的LDPC编解码中的校验矩阵作为压缩感知重构的测量矩阵进行压缩感知重构解码。实验结果表明,在高误码率的情况下,基于CS的LDPC码解码方法相对于传统LDPC解码方法的抗误效果更明显,为视频的抗误传输提供了新的思路。     

CVS中基于视频运动特征的多假设-双稀疏重构算法

针对目前视频压缩感知重构算法对不同特征的视频序列重构质量参差不齐的问题,结合双稀疏对轮廓、细节的高清晰重构以及多假设算法对高频噪声有效抑制的优点,本文提出一种基于视频运动特征的多假设-双稀疏重构算法（VF-MH-DSR）.基本思路是基于每个视频组（GOP）的运动特征,采取相应的多假设-双稀疏重构策略.首先给出一种观测域多维度参考帧的多假设重构算法（MD-MRF-MH）及其最优相似块个数设置方案;然后给出一种像素域多假设参考帧的重构算法（PD-MRF-MH）及一种高性能双匹配准则;最后介绍了视频信号运动特征判定方案及多假设-双稀疏重构的具体实现方案.仿真实验表明,本文所提多假设-双稀疏重构算法相对于目前较好的多假设预测重构算法2sMHR及组稀疏重构算法SSIM-InterF-GSR,重构性能平均提升了1.98dB和0.84dB.     

采用双网络结构的压缩视频超分辨率重建

在实际应用中,为了节省带宽和方便存储,图像和视频通常被下采样和压缩,而降质的图像与视频无法满足人们的实际需求。针对这一问题,采用了一种双网络结构的超分辨率重建方法,首先建立下采视频与压缩后的低分辨率视频的映射关系,然后建立质量增强的压缩视频与原始视频的映射关系,最终在输出端可以得到质量提升的视频帧。在网络中,采用密集残差块来提取压缩视频中丰富的局部分层特征,并结合全局残差学习恢复视频中的高频信息。在压缩环节,采用高性能视频编码来验证所提算法的有效性。实验结果表明,相比于主流的视频编码标准和先进的超分辨率重建算法,所提方法能有效提升编码视频的率失真性能。     

Image representation by compressive sensing for visual sensor networks

This paper addresses the image representation problem in visual sensor networks. We propose a new image representation method for visual sensor networks based on compressive sensing (CS). CS is a new sampling method for sparse signals, which is able to compress the input data in the sampling process. Combining both signal sampling and data compression, CS is more capable of image representation for reducing the computation complexity in image/video encoder in visual sensor networks where computation resource is extremely limited. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly decomposed into two components, i.e., dense and sparse components; then the dense component is encoded by the traditional approach (JPEG or JPEG 2000) while the sparse component is encoded by a CS technique. In order to improve the rate distortion performance, we leverage the strong correlation between dense and sparse components by using a piecewise autoregressive model to construct a prediction of the sparse component from the corresponding dense component. Given the measurements and the prediction of the sparse component as initial guess, we use projection onto convex set (POCS) to reconstruct the sparse component. Our method considerably reduces the number of random measurements needed for CS reconstruction and the decoding computational complexity, compared to the existing CS methods. In addition, our experimental results show that our method may achieves up to 2 dB gain in PSNR over the existing CS based schemes, for the same number of measurements.     

基于最优观测矩阵的压缩信道感知

信道估计技术作为获得信道衰落信息的方法,是提高无线信道传输接收性能的关键技术。而物理多径信道固有的稀疏性,使得将压缩感知（CS）理论用于稀疏多径信道的估计成为可能。由于传统的线性估计方法没有考虑信道的固有稀疏性,因而在训练序列数目较少的情况下,压缩信道估计的重构效果要明显优于传统的最小二乘估计方法,在获得同样估计性能的情况下,需要的训练序列长度也大大减少,提高了频谱资源利用率,体现了压缩信道估计出色的估计性能。本文在应用CS理论进行稀疏信道估计的过程中,通过减小观测矩阵的列向量相关性,产生最优观测矩阵的方法,从而让压缩信道估计的性能得到进一步的改善。      

Dictionary learning based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient reconstruction by leveraging more realistic signal models that go beyond simple sparsity is still an open challenge. In this paper, we propose a novel “undersampled” correlation noise model to describe compressively sampled video signals, and present a maximum-likelihood dictionary learning based reconstruction algorithm for DCVS, in which both the correlation and sparsity constraints are included in a new probabilistic model. Moreover, the signal recovery in our algorithm is performed during the process of dictionary learning, instead of being employed as an independent task. Experimental results show that our proposal compares favorably with other existing methods, with 0.1–3.5 dB improvements in the average PSNR, and a 2–9 dB gain for non-key frames when key frames are subsampled at an increased rate.     

A statistical image resolution enhancement approach for mixed-resolution video

Many scalable video compression techniques utilise a mixed-resolution scheme, which down-samples some frames at the encoder to produce reduced-resolution frames while keeping resolutions of other frames unchanged as full resolutions, in order to achieve higher compression gain. Image enlargement technique is required at the decoder to recover the original full-resolution frames for this mixed-resolution video system set-up. This article proposes a Bayesian approach to enlarge the reduced-resolution frame via its maximum a-posterior estimation, using the information from the observed reduced-resolution frame, plus more detailed information extracted from available neighbouring frames in full resolution. Experiments are conducted to justify that the proposed approach outperforms a few conventional approaches.     

Flexible distribution of complexity by hybrid predictive-distributed video coding

There is currently limited flexibility for distributing complexity in a video coding system. While rate-distortion-complexity (RDC) optimization techniques have been proposed for conventional predictive video coding with encoder-side motion estimation, they fail to offer true flexible distribution of complexity between encoder and decoder since the encoder is assumed to have always more computational resources available than the decoder. On the other hand, distributed video coding solutions with decoder-side motion estimation have been proposed, but hardly any RDC optimized systems have been developed.To offer more flexibility for video applications involving multi-tasking or battery-constrained devices, in this paper, we propose a codec combining predictive video coding concepts and techniques from distributed video coding and show the flexibility of this method in distributing complexity. We propose several modes to code frames, and provide complexity analysis illustrating encoder and decoder computational complexity for each mode. Rate distortion results for each mode indicate that the coding efficiency is similar. We describe a method to choose which mode to use for coding each inter frame, taking into account encoder and decoder complexity constraints, and illustrate how complexity is distributed more flexibly.     

Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video

Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity.     

A video error concealment method using data hiding based on compressed sensing over lossy channel

The video error concealment with data hiding (VECDH) method aims to conceal video errors due to transmission according to the auxiliary data directly extracted from the received video file. It has the property that can well reduce the error propagated between spatially/temporally correlated macro-blocks. It is required that, the embedded information at the sender side should well capture/reflect the video characteristics. Moreover, the retrieved data should be capable of correcting video errors. The existing VECDH algorithms often embed the required information into the corresponding video frames to gain the transparency. However, at the receiver side, the reconstruction process may loss important information, which could result in a seriously distorted video. To improve the concealment performance, we propose an efficient VECDH algorithm based on compressed sensing (CS) in this paper. For the proposed method, the frame features to be embedded in every video frame are generated from the frame residuals CS measurements and scrambled with other frame features as marked data. The marked data is embedded into the corresponding frames by modulating color-triples for its least impacts on the carriers. For the receiver, the extracted data is used to reconstruct residuals to conceal errors. Error positions are located using the set theory. Since the CS has the ability to sample a signal within a lower sampling rate than the Shannon–Nyquist rate, the original signal could be reconstructed very well in theory. This indicates that the proposed method could benefit from the CS, and therefore keep better error concealment behavior. The experimental results show that the PSNR values gain about 10 dB averagely and the proposed scheme in this paper improves the video quality significantly comparing with the exiting VECDH schemes.     

CS与GPR联合反演目标成像

压缩传感(CS)理论是在已知信号具有稀疏性或可压缩性的条件下对信号数据进行采集、编解码的新理论。压缩传感采用非自适应线性投影来保持信号的原始结构,能通过数值最优化问题准确重构原始信号。压缩传感以远低于奈奎斯特频率进行采样,在高分辨压缩成像系统、视频图像采集系统、雷达成像以及MRI医疗成像等领域有着广阔的应用前景。阐述了压缩传感理论框架以及信号稀疏表示、CS编解码模型,并进行了压缩传感与探地雷达联合反演目标成像。反演结果表明,随机孔径压缩传感成像算法比递归反向投影算法和最小二乘法所需数据量少,成像效果好,目标旁瓣小,对噪声的鲁棒性更好。     

帧间自适应语音信号压缩感知

近年来提出的压缩感知是一种以低于传统奈奎斯特速率对信号采样可得到精确恢复的理论。该理论很快应用于简化传统的采样硬件、缩短采样时间、以及减少数据的存储空间。针对语音信号的传输问题,本文提出一种帧间自适应语音信号压缩感知的方法。在离散余弦变换域的语音信号具有稀疏性的前提下,以大量语音信号帧的分析统计为依据,提出一种基于语音帧能量分级和帧间位置惯性的语音信号自适应压缩感知算法。实验结果表明,能量自适应可以显著地提高语音信号的恢复质量,而位置自适应可以明显地减少语音信号的恢复时间,从而本文提出的算法可以用较少的恢复时间获得较好的恢复效果。