INTRA-FRAME PREDICTION ALGORITHM FOR H.264 BASED ON TEMPORAL AND SPATIAL CORRELATIONS

为了降低H．264编码复杂度，提出一种基于时空相关性的快速帧内预测算法。该算法充分利用视频序列的时间相关性，自适应地根据先前编码宏块对当前宏块进行预判，在Intra＿4×4和Intra＿16×16预测方式之间进行选择；对Intra＿4×4预测方式，依据模式聚集特性，参考先前帧和当前帧已编码宏块的预测模式统计特征，确定当前4×4子块的候选模式。实验结果表明，该算法在保证信噪比和比特率几乎不变的情况下，编码时间减少约40％，提高了编码效率。 In order to reduce the encoding complexity in H.264,we propose a fast intra-frame prediction algorithm which is based on temporal and spatial correlations.Making full use of the advantage of temporal correlation of video sequence,the algorithm adaptively prejudges current macroblock according to previous coded macroblocks,and selects between the prediction approaches of Intra_4 ×4 and Intra_16 ×16;For Intra_4 ×4 prediction,it decides the candidate modes for current 4 ×4 sub-macroblock according to mode-gathering characteristic and referring the statistic characteristics of prediction mode of coded macroblocks in pre-frame and current frame.Experimental result shows that the algorithm reduces the encoding time by 40% with a neglectable PSNR change and bit rate increase,improves encoding efficiency.In order to reduce the encoding complexity in H.264, we propose a fast intra-frame prediction algorithm which is based on temporal and spatial correlations. Making full use of the advantage of temporal correlation of video sequence, the algorithm adaptively prejudges current macroblock according to previous coded macroblocks, and selects between the prediction approaches of Intra_4*4 and Intra_16*16; For Intra_4*4 prediction, it decides the candidate modes for current 4*4 sub-macroblock according to mode-gathering characteristic and referring the statistic characteristics of prediction mode of coded macroblocks in pre-frame and current frame. Experimental result shows that the algorithm reduces the encoding time by 40% with a neglectable PSNR change and bit rate increase, improves encoding efficiency

design and implementation of real-time process on rtems

以嵌入式实时操作系统RTEMS为原型设计实现了实时进程.将RTEMS操作系统内核与应用程序隔离,使二者运行在不同的特权级别下,应用程序的错误不会导致操作系统崩溃,从而提高了整个系统的可靠;同时对内核内存管理和地址映射机制进优化,保证原有系统的性能.从系统的可靠性、实时性以及设计实现的复杂性等方面进行试验和分析,试验结果表明,实时进程的设计与实现提高了系统可靠性,并且能够保证原有系统实时性和简单性

design and implementation of partition embedded system based on para-virtualization

针对嵌入式系统的高可靠性和高安全性要求,设计了基于半虚拟技术的分区系统。在RTEMS实时操作系统中,使用半虚拟技术实现虚拟机监视器,给分区操作系 统PLINUX创造虚拟环境。通过地址映射机制和分区调度,把分区系统在空间和时间上隔离开,可有效阻止分区的错误传播,提高系统的容错能力。对系统可靠 性、调度效率等方面进行试验和分析,实验结果表明,该分区系统的可靠性明显提高,而且继承了RTEMS的实时性和设计的简洁性。To meet the requirement of high security and reliability of embedded system,a design of partition system based on para-virtualization is proposed.In RTEMS real-time operating system,virtual machine monitor is realized using para-virtualization technology,it provides virtual environment for PLINUX.Through address mapping mechanism of CPU and partition scheduling,the partition is separated in space and time,which effectively prevents partitions error propagation and improves the systems fault tolerance.System reliability and scheduling are measured,test results are given,which show that partition system preserves the real-time feature of RTEMS,while improving its reliability

a high performance, fully pipelined and parameter controllable implementation of jpeg-ls encoder

提出了一种高性能的JPEG-LS无损/近无损图像压缩算法VLSI实现结构.通过对JPEG-LS算法瓶颈的分析,针对算法中不利于流水线实现的场景缓存部分,采用了一种信号量集机制避免流水线等待.全流水线结构保证了算法实现可以满足高速图像传感器系统的吞吐量需求.同时通过高度参数化的设计,系统可以动态调整和优化算法参数,使压缩效果和效率适应不同的运行环境.算法在FPGA平台通过验证,并得到了接近甚至超过其他A-SIC实现的性能

variable recovery at instruction-level for fault tolerance

提出一种抵抗瞬时故障的自动编译容错恢复方法,用源码中的变量信息在指令级别进行冗余错误流裁剪,在LCC上加以实现,并获得良好的容错性能。实验结果表明,该方法仅增加0.043倍的时间损耗及0.69倍的空间损耗,在时空损耗上优于现有的其他方法

temporal algorithm for ir small targets detection

为了解决红外小目标检测问题,提出了时域红外小目标检测方法.在图像序列中背景像素、目标像素以及杂波像素的时域差分模型基础上提出了红外小目标时域检测算法,算法共分为两步:相关检测和广义似然比检测.经过相关检测后,图像序列中的噪声几乎完全被门限所抑制,只有极少数噪声像素、全部目标像素和一小部分杂波像素可以通过相关检测门限.为了进一步从通过相关检测门限的像素中检测出目标,又提出了一种新的广义似然比检测方法.这种经过改进的广义似然比检测能够进一步抑制噪声和杂波,提高检测的性能.分析表明,时域目标检测算法能够以很高

Decomposition of Interference Hyperspectral Images Using Improved Morphological Component Analysis

干涉高光谱图像特殊的成像原理, 使其帧内存在着大幅值且位置固定的干涉条纹, 而帧间存在着水平移位的背景图像, 这种特点会严重的破坏原始图像的固有结构, 从而导致新兴的压缩感知理论与传统压缩算法的直接应用无法得到理想的效果。 由于干涉条纹信息与背景图像信息的特征不同, 能够对干涉条纹与背景图像进行稀疏表示的正交基也是不同的。基于这种思想, 使用MCA(morphological component analysis)算法对干涉高光谱图像中干涉条纹信息与背景图像信息进行分离处理。 由于干涉高光谱图像数据量庞大, 传统的MCA算法对干涉高光谱数据的图像分解, 迭代收敛速度慢, 运算效率较低, 故而针对干涉高光谱数据特点对传统MCA算法进行改进, 改变其迭代收敛条件, 当分离后的图像信号与原始图像信号的误差已经基本保持不变时, 即终止迭代; 并根据对应正交基能且仅能稀疏表示对应信号的思想, 对阈值采用自适应的方式进行更新, 在新的阈值更新模式中, 图像信号在不同正交基下的映射系数被计算与比较。 大量实验结果表明, 对于LASIS数据与LAMIS数据, MCA算法都能够较完美的将干涉高光谱图像分解, 改进的MCA算法更能在保持完美分解输出结果的同时, 相对于传统MCA方法显著的减小迭代次数, 更快的达到迭代收敛条件, 从而有效的提高了算法的运算效率与实时性需求, 也为新兴的压缩感知理论在干涉高光谱图像中的进一步应用提供了一种很好的解决方案。As the special imaging principle of the interference hyperspectral image data, there are lots of vertical interference stripes in every frames. The stripes'positions are fixed, and their pixel values are very high. Horizontal displacements also exist in the background between the frames. This special characteristics will destroy the regular structure of the original interference hyperspectral image data, which will also lead to the direct application of compressive sensing theory and traditional compression algorithms can't get the ideal effect. As the interference stripes signals and the background signals have differentcharacteristics themselves, the orthogonal bases which can sparse represent them will also be different. According to this thought, in this paper the morphological component analysis (MCA) is adopted to separate the interference stripessignals and background signals. As the huge amount of interference hyperspectral image will lead to slow iterative convergence speed and low computational efficiency of the traditional MCA algorithm, an improved MCA algorithm is also proposed according to the characteristics of the interference hyperspectral image data, the conditions of iterative convergence is improved, the iteration will be terminated when the error of the separated image signals and the original image signals are almost unchanged. And according to the thought that the orthogonal basis can sparse represent the corresponding signals but cannot sparse represent other signals, an adaptive update mode of the threshold is also proposed in order to accelerate the computational speed of the traditional MCA algorithm, in the proposed algorithm, the projected coefficients of image signals at the different orthogonal bases are calculated and compared in order to get the minimumvalue and the maximum value of threshold, and the average value of them is chosen as an optimal threshold value for the adaptive update mode. The experimental results prove that whether LASIS and LAMIS image data, the traditional MCA algorithm can separate the interference stripes signals and background signals very well, and make the interference hyperspectral image decomposition perfectly,and the improved MCA algorithm not only keep the perfect results of the traditional MCA algorithm, but also can reduce the times of iteration and meet the iterative convergence conditions much faster than the traditional MCA algorithm, which will also provide a very good solution for the new theory of compressive sensing

An adaptive wavelet transformation used on interference hyperspectral image compression

为了更好地提高干涉高光谱图像的压缩性能，针对干涉成像光谱仪的成像原理，提出了一种自适应方向预测提升小波变换的方法，在帧序列方向的提升小波变换中，以自适应方向获得最佳预测值，并且改变传统三维提升小波的变换顺序，消除大部分干涉条纹冗余，大量实验证明本文方法得到的高频子带小波系数相对于传统方法在指定码率的情况下重构图像可以获得更高的信噪比，恢复的光谱曲线具有更小的均方误差。 To get better performance of interference hyperspectral image compression, according to the imaging principle ofinterference hyperspectral image, we propose an adaptive direction prediction wavelet transform forLWT ( Lifting Wavelet Transformation ) , to get the best predicted values through adaptive direction in the LWT on the frame direction, and change the order of the traditional wavelet transform. The experiment results prove that the proposed method can get higher SNR at the same bpppb compared with the traditional method, and can get smaller MSE in the recovered spectral curve.To get better performance of interference hyperspectral image compression, according to the imaging principle ofinterference hyperspectral image, we propose an adaptive direction prediction wavelet transform forLWT (Lifting Wavelet Transformation), to get the best predicted values through adaptive direction in the LWT on the frame direction, and change the order of the traditional wavelet transform. The experiment results prove that the proposed method can get higher SNR at the same bpppb compared with the traditional method, and can get smaller MSE in the recovered spectral curve

红外弱小目标检测跟踪算法研究

首先对红外图像进行建模,然后在此基础上提出一套目标检测跟踪算法,给出了实验结果.实验结果表明,算法能够很好地完成对信噪比大于4目标的检测,同时算法具有多目标检测能力.当确定需要跟踪的目标以后,系统转入目标跟踪,进而完成对所关注目标的跟踪任务

a hybrid fault-tolerant scheduling algorithm for multiprocessor in space environment

为提高空间应用环境中混合实时任务的容错调度效率,提出基于空间环境的多处理器混合容错调度算法。算法在周期任务调度中采用基于分组的"最佳适应"分配策略,以少量增加的计算时间获得更为紧凑的任务调度结果,从而减少调度所需的处理器数目和任务执行时间;在非周期任务调度中,利用基于空闲时间片的方法完成动态调度,使得混合实时任务的容错调度效率有显著提高,能够更好地适应复杂空间环境的任务处理。仿真结果表明算法在改善混合实时任务容错调度性能上具有重要作用。国家高技术研究发展计划(2012AA011206)In order to improve the efficiency of fault-tolerant scheduling of hybrid real-time tasks in circumstance of space application,a hybrid fault-tolerant scheduling algorithm for multiprocessor in space environment is proposed. In this algorithm,a grouping based Best-Fit assignment strategy in the periodic task scheduling is used to obtain more compact scheduling result by use of a little increasing computation time,so that the number of processors and the task execution time required by the algorithm are saved. In addition,a time slice based approach is used to finish the dynamic scheduling of aperiodic tasks,thus improving the efficiency of fault-tolerant scheduling of hybrid real-time tasks and making the algorithm more suitable for the task processing in complicated space environment. It is shown from the simulation that this algorithm has important effect on improving the performance of fault-tolerant scheduling of hybrid real-time tasks