Video compression employing algorithm using temporal blockingstructures

A new video compression algorithm based on a temporal blocking structure, rather than the more conventional spatial blocking structure, is described. This blocking structure forms the basis of an adaptive vector quantisation (VQ) algorithm, the performance of which is then compared with a similar adaptive VQ scheme based on a spatial blocking structure     

An efficient content-adaptive motion-compensated 3-D DWT with enhanced spatial and temporal scalability.

We propose a novel, content adaptive method for motion-compensated three-dimensional wavelet transformation (MC 3-D DWT) of video. The proposed method overcomes problems of ghosting and nonaligned aliasing artifacts which can arise in regions of motion model failure, when the video is reconstructed at reduced temporal or spatial resolutions. Previous MC 3-D DWT structures either take the form of MC temporal DWT followed by a spatial transform ("t+2D"), or perform the spatial transform first ("2D + t"), limiting the spatial frequencies which can be jointly compensated in the temporal transform, and hence limiting the compression efficiency. When the motion model fails, the "t + 2D" structure causes nonaligned aliasing artifacts in reduced spatial resolution sequences. Essentially, the proposed transform continuously adapts itself between the "t + 2D" and "2D + t" structures, based on information available within the compressed bit stream. Ghosting artifacts may also appear in reduced frame-rate sequences due to temporal low-pass filtering along invalid motion trajectories. To avoid the ghosting artifacts, we continuously select between different low-pass temporal filters, based on the estimated accuracy of the motion model. Experimental results indicate that the proposed adaptive transform preserves high compression efficiency while substantially improving the quality of reduced spatial and temporal resolution sequences.     

A new video watermarking algorithm based on 1D DFT and Radon transform

In this paper, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional discrete Fourier transform) and Radon transform. The 1D DFT for a video sequence generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The adaptive embedding strength for different locations keeps the fidelity of the watermarked video. The performance of the proposed algorithm is evaluated by video compression standard H.264 with three different bit rates; geometric attacks such as rotation, translation, and aspect-ratio changes; and other attacks like frame drop, frame swap, spatial filtering, noise addition, lighting change, and histogram equalization. The main contributions of this paper are the introduction of the 1D DFT along temporal direction for watermarking that enables the robustness against video compression, and the Radon transform-based watermark embedding and extraction that produces the robustness against geometric transformations. One of the most important advantages of this video watermarking algorithm is its simplicity and practicality.     

An adaptive temporal-spatial filter for MPEG decoded video signals

A new adaptive post-processing algorithm for the MPEG decoded video sequences is proposed. We use a motion compensated averaging filter to reduce the noises in the temporal domain and an adaptive spatial filter to remove noise in the spatial domain and preserve the edge of different orientations in the image. A MPEG decoded video sequence called table tennis is processed by our proposed filter. The post-processed video sequence shows that its image quality is improved, especially of the moving objects.     

Practical transform coding of multispectral imagery

We present an implementable three dimensional terrain adaptive transform based bandwidth compression technique for multispectral imagery. The algorithm exploits the inherent spectral and spatial correlations in the data. The compression technique is based on Karhunen-Loeve transformation for spectral decorrelation followed by the standard JPEG algorithm for coding the resulting spectrally decorrelated eigen images. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near-lossless at about 5:1 CR to visually lossy beginning at about 30:1 CR. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral correlation transformation as a function of the variation of the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a alternate spatial coding procedure. The significant practical advantage of this proposed approach is that it is based on the standard and highly developed JPEG compression technology     

自适应时空正则化的相关滤波目标跟踪

针对相关滤波目标跟踪算法空间正则权重没有与目标建立联系和时间正则项不能自适应更新的问题,提出自适应时空正则化的相关滤波目标跟踪算法。首先,利用初始帧的显著感知参考权重,使自适应空间正则项能够在后续跟踪过程中获取与目标存在联系的空间正则权重。然后,利用相邻两帧响应得分的变化情况计算时间正则化参数的参考值,使自适应时间正则项可以通过变化的正则化参数不断更新。最后,采用交替方向乘子法(ADMM)优化算法,以较少的迭代次数分别求解出滤波器函数、空间正则权重和时间正则化参数。在OTB-2015数据集上进行实验,结果表明本文算法的跟踪性能优于其他对比算法,其中距离精度和成功率分别达到86.4%和65.6%,且本文算法在具有形变、旋转、遮挡和出视野等属性的复杂跟踪场景下更具鲁棒性。     

Real-Time Video Compression Algorithm for Hadamard Transform Processing

A real-time digital video processor using Hadamard transform techniques to reduce video bandwidth is described. The processor can be programmed with different parameters to investigate various algorithms for bandwidth compression. The processor is also adaptive in that it can select different parameter sets to trade-off spatial resolution for temporal resolution in the regions of the picture that are moving. Algorithms used in programming the system are described along with results achieved at various levels of compression. The algorithms relate to spatial compression, temporal compression, and the adaptive selection of parameter sets.     

A Novel Adaptive Antenna Array for DS/CDMA Code Acquisition

Equipped with an adaptive beamformer, existing adaptive array code acquisition still relies on the correlator structure. Due to the inherent property of the associated serial-search scheme, its mean acquisition time is large, especially in strong interference environments. In this paper, we propose a novel adaptive filtering scheme to solve the problem. The proposed scheme comprises two adaptive filters, an adaptive spatial and an adaptive temporal filter. With a specially designed structure, the spatial filter can act as a beamformer suppressing interference, while the temporal filter can act as a code-delay estimator. A mean squared error (MSE) criterion is proposed such that these filters can be simultaneously adjusted by a stochastic gradient descent method. The performance as well as the convergence behavior of the proposed algorithm are analyzed in detail. Closed-form expressions for optimum filter weights, optimum beamformer signal-to-interference-plus-noise ratio (SINR), steady-state MSE, and mean acquisition time are derived for the additive white Gaussian noise (AWGN) channel. Computer simulations show that the mean acquisition time of the proposed algorithm is much shorter than that of the correlator-based approach, and the derived theoretical expressions are accurate.     

基于聚类模型预测的无线传感网自适应采样技术研究

该文利用无线传感网(WSNs)的数据空间相关性,提出一种基于数据梯度的聚类机制,聚类内簇头节点维护簇成员节点的数据时间域自回归(AR)预测模型,在聚类内范围实施基于预测模型的采样频率自适应算法。通过自适应优化调整采样频率,在保证数据采样精度的前提下减少了冗余数据传输,提高无线传感网的能效水平。该文提出的时间域采样频率调整算法综合考虑了感知数据的时空联合相关性特点,仿真结果验证了该文算法的性能优势。     

An adaptive spatio-temporal perception aware quantization algorithm for AVS2

Adaptive quantization proves to be an effective tool to improve coding performance. In this paper, we propose an adaptive spatiotemporal perception aware quantization algorithm to increase subjective coding performance. To measure the spatiotemporally perceptual redundancy, the perceptual complexity models are firstly established with spatial and temporal characteristics respectively. With the help of the models, the adaptive spatial and temporal quantization parameter (QP) offsets are then calculated for each coding tree unit (CTU), respectively. Finally, the perceptually optimal Lagrange multiplier of each CTU is determined with the spatial–temporal QP offset. Experimental results show that the proposed algorithm reduces 8.6% and 8.4% Bjontegaard-Delta Rate (BD-Rate) with Structural Similarity Index Metric (SSIM) in average over the second generation of Audio Video Coding Standard (AVS2) reference software RD17.0 in Low-Delay-P (LDP) and Random-Access (RA) configurations, respectively. The subjective assessment proves that the proposed algorithm can reduce the bitrates with the same subjective quality significantly.     

MODIS多光谱图像压缩研究

为有效存储MODIS多光谱图像数据,该文提出一种基于谱间预测和整数小波变换的多光谱图像压缩算法.首先通过构造谱间最优预测器去除谱间冗余,再利用整数小波变换和SPIHT算法对预测误差图像去除空间冗余,最后进行自适应算术编码.该方法可实现MODIS多光谱图像的无损、近无损和有损压缩,取得了满意的实验结果;在不同小波基条件下与3D-SPIHT算法比较,表明了该方法的有效性.     

Efficient Mode Decision Algorithm Based on Spatial,Temporal, and Inter‐layer Rate‐Distortion Correlation Coefficients for Scalable Video Coding

The layered coding structure of scalable video coding (SVC) with adaptive inter‐layer prediction causes noticeable computational complexity increments when compared to existing video coding standards. To lighten the computational complexity of SVC, we present a fast algorithm to speed up the inter‐mode decision process. The proposed algorithm terminates inter‐mode decision early in the enhancement layers by estimating the rate‐distortion (RD) cost from the macroblocks of the base layer and the enhancement layer in temporal, spatial, and inter‐layer directions. Moreover, a search range decision algorithm is also proposed in this paper to further increase the motion estimation speed by using the motion vector information from temporal, spatial, or inter‐layer domains. Simulation results show that the proposed algorithm can determine the best mode and provide more efficient total coding time saving with very slight RD performance degradation for spatial and quality scalabilities.     

Dynamic image data compression in spatial and temporal domains:theory and algorithm

Advanced medical imaging requires storage of large quantities of digitized clinical data. These data must be stored in such a way that their retrieval does not impair the clinician's ability to make a diagnosis. We propose a theory and algorithm for near lossless dynamic image data compression. Taking advantage of domain-specific knowledge related to medical imaging, medical practice and the dynamic imaging modality, a compression ratio greater than 80:1 is achieved. The high compression ratios are achieved by the proposed algorithm through three stages: (1) addressing temporal redundancies in the data through application of image optimal sampling, (2) addressing spatial redundancies in the data through cluster analysis, and (3) efficient coding of image data using standard still-image compression techniques. To illustrate the practicality of the algorithm, a simulated positron emission tomography (PET) study using the fluoro-deoxy-glucose (FDG) tracer is presented. Realistic dynamic image data are generated by virtual scanning of a simulated brain phantom as a real PET scanner. These data are processed using the conventional and proposed algorithms as well as the techniques for storage and analysis. The resulting parametric images obtained from the conventional and proposed approaches are subsequently compared to evaluate the proposed compression algorithm. The storage space for dynamic image data reduced by more than 95%, without loss in diagnostic quality. Therefore, the proposed theory and algorithm are expected to be very useful in medical image database management and telecommunication     

Cellular multiadaptive analogic architecture: a computational framework for UAV applications

An efficient adaptive algorithm in real-time applications should make optimal use of the available computing power for reaching some specific design goals. Relying on appropriate strategies, the spatial resolution/temporal rate can be traded against computational complexity; and sensitivity traded against robustness, in an adaptive process. In this paper, we present an algorithmic framework where a spatial multigrid computing is placed within a temporal multirate structure, and at each spatial grid point, the computation is based on an adaptive multiscale approach. The algorithms utilize an analogic (analog and logic) architecture consisting of a high-resolution optical sensor, a low-resolution cellular sensor-processor and a digital signal processor. The proposed framework makes the acquisition of a spatio-temporally consistent image flow possible even in case of extreme variations (relative motion) in the environment. It ideally supports the handling of various difficult problems on a moving platform including terrain identification, navigation parameter estimation, and multitarget tracking. The proposed spatio-temporal adaptation relies on a feature-based optical-flow estimation that can be efficiently calculated on available cellular nonlinear network (CNN) chips. The quality of the adaptation is evaluated compared to nonadaptive spatio-temporal behavior where the input flow is oversampled, thus resulting in redundant data processing with an unnecessary waste of computing power. We also use a visual navigation example recovering the yaw-pitch-roll parameters from motion-field estimates in order to analyze the adaptive hierarchical algorithmic framework proposed and highlight the application potentials in the area of unmanned air vehicles.     

A Blind Space-Time Constrained Minimum Output Energy Detector for DS-CDMA Communication System

In this paper, a blind space-time (ST) multiuser detector, called ST constrained minimum output energy (ST-CMOE) detector, is proposed. It can be viewed as an extension of the CMOE method ( Proc. Globecom, p. 379, 1994), and therefore it is immune to near-far effect. It processes the antenna array output jointly in spatial and temporal domain. Simulation results show that the proposed algorithm can outperform the space-CMOE detector proposed by Wang and Poor ( IEEE Trans. Signal Process. vol. 47, p. 2356, 1999) which process the received signal in spatial and temporal domain separately. It can also outperform the ST subspace detector proposed by Chkeif (IEEE Trans. Commun. vol. 48, p. 729, 2000), which, although processes the received signal jointly in spatial and temporal domains, suffers from the mismatch problem in the multipath environment. The simulation results also show that the proposed ST-CMOE detector can achieve the same performance as the training-based ST minimum-mean-square-error detector. In order to reduce the computational load and adapt to dynamic multiple-access channels, an adaptive algorithm is presented. The computational load of this adaptive detector is comparable to Hu's method (IEEE Trans. Signal Process . vol. 52, p. 1862, 2004), while it does not require any initial training or channel estimation in the multipath environment. Simulation results show that the proposed adaptive ST-CMOE detector can work well in a dynamic multiple-access channel, where interferences may enter or leave the channel at any time     

Lossless compression of video sequences

We investigate lossless compression schemes for video sequences. A simple adaptive prediction scheme is presented that exploits temporal correlations or spectral correlations in addition to spatial correlations. It is seen that even with motion compensation, schemes that utilize only temporal correlations do not perform significantly better than schemes that utilize only spectral correlations. Hence, we look at hybrid schemes that make use of both spectral and temporal correlations. The hybrid schemes give significant improvement in performance over other techniques. Besides prediction schemes, we also look at some simple error modeling techniques that take into account prediction errors made in spectrally and/or temporally adjacent pixels in order to efficiently encode the prediction residual. Implementation results on standard test sequences indicate that significant improvements can be obtained by the proposed techniques     

基于三维自适应预测的高光谱图像无损压缩算法

利用高光谱图像具有较强的谱间相关性的特点,本文提出一种基于三维自适应预测的高光谱图像无损压缩方法,首先根据相关系数计算波段预测顺序,然后利用相关性较强的空间邻点和谱间邻点,采用基于神经网络模型的自适应预测方法进行三维预测编码.实验结果表明,该方法能够有效的去除高光谱图像的空间和谱间相关性,与现在最优的无损压缩国际标准JPEG-LS相比,压缩后的平均比特率能够降低0.3bpp左右.     

基于能量树编码的小波图像压缩算法

针对小波变换的空间能量聚集特性,提出了一种基于能量树编码的小波图像压缩算法。该算法在离散小波变换的基础上,分别对图像的各高频子带按其局部能量构建分层能量树,利用总能量和各层的能量角等效表示子带的小波系数;根据给定的压缩比,选择合适的代价函数构建最佳能量树,然后对其进行量化和编码,通过自适应的比特率分配实现小波图像压缩。实验结果表明,该算法实现简单,重构图像质量好,与当前多种主流的小波图像压缩算法相比,压缩性能有了明显提高。     

传感器网络中一种基于一元线性回归模型的空时数据压缩算法

针对传感器网络中节点采样数据的空间和时间冗余特点以及节能要求,该文提出了一种基于一元线性回归模型的空时数据压缩算法ODLRST。ODLRST先在每个节点内进行消除时间冗余的数据压缩,再在节点汇集处对来自不同节点的数据消除空间冗余以进一步压缩数据。仿真实验证明,ODLRST能够极大地减少节点发送的数据量和网络中的通信流量,节省并平衡网络中的能量消耗。     

Low bit-rate video compression with neural networks and temporalsubsampling

In this paper we describe a novel neural network technique for video compression, using a “point-process” type neural network model we have developed which is closer to biophysical reality and is mathematically much more tractable than standard models. Our algorithm uses an adaptive approach based upon the users' desired video quality Q, and achieves compression ratios of up to 500:1 for moving gray-scale images, based on a combination of motion detection, compression, and temporal subsampling of frames. This leads to a compression ratio of over 1000:1 for full-color video sequences with the addition of the standard 4:1:1 spatial subsampling ratios in the chrominance images. The signal-to-noise ratio ranges from 29 dB to over 34 dB. Compression is performed using a combination of motion detection, neural networks, and temporal subsampling of frames. A set of neural networks is used to adaptively select the desired compression of each picture block as a function of the reconstruction quality. The motion detection process separates out regions of the frame which need to be retransmitted. Temporal subsampling of frames, along with reconstruction techniques, lead to the high compression ratios