An Efficient Block Entropy Based Compression Scheme for Systems-on-a-Chip Test Data

The emergence of the nanometer scale integration technology made it possible for systems-on-a-chip, SoC, design to contain many reusable cores from multiple resources. This resulted in higher complexity SoC testing than the conventional VLSI. To address this increase in design complexity in terms of data-volume and test-time, several compression methods have been developed, employed and proposed in the literature. In this paper, we present a new efficient test vector compression scheme based on block entropy in conjunction with our improved row-column reduction routine to reduce test data significantly. Our results show that the proposed method produces much higher compression ratio than all previously published methods. On average, our scheme scores nearly 13% higher than the best reported results. In addition, our scheme outperformed all results for each of the tested circuits. The proposed scheme is very fast and has considerable low complexity.     

An Efficient Compressive Sensing Routing Scheme for Internet of Things Based Wireless Sensor Networks

Wireless Personal Communications - Internet of Things (IoT) integrates diverse types of sensors, mobiles and other technologies to physical world and IoT technology is used in a wide range of...     

SAR图像压缩采样恢复的GPU并行实现

压缩采样(CS)技术被尝试应用于合成孔径雷达(SAR)图像的压缩。然而,高分辨SAR图像数据量大,导致压缩采样后的恢复过程计算量大,传统的中央处理器(CPU)无法实时成像。为解决这一问题,该文在图形处理器(GPU)平台上设计了CS的并行方法,并实现了SAR图像压缩。实验结果表明,在保证SAR图像压缩性能的前提下,该文设计的GPU并行处理速度能够提高到CPU串行处理的8.8倍。     

一种基于自适应预测的医学图像高效无损压缩方法

随着数字化医学图像海量的增长及PACS系统的广泛应用,对医学图像进行高效的无损压缩已成为广泛关注的问题.本文提出一种基于自适应预测的无损压缩方法,该方法利用神经网络模型自学习的能力,自适应的调整预测器的预测系数.实验表明,该方法能有效去除X线医学图像的空间相关性,还能同时去除彩色医学图像的空间和谱间相关性,取得较高的压缩比,且编解码速度较高.     

LCD缺陷检测系统的图像压缩感知方法

图像采集是液晶显示屏(LCD)缺陷检测系统的关键步骤,而高清屏图像的大数据量给检测系统的实时数据采集、传输和处理造成了很大压力。为解决这一问题,本文提出了一种先行压缩采样后列压缩采样的压缩感知方法,并给出了一种LCD缺陷图像的压缩感知电路实现方案,该电路主要由压缩采样、控制、图像转置存储等模块组成。这种方法充分考虑了图像整体的稀疏性,提高了压缩效率,且结构简单,易于硬件实现。在FPGA上的仿真实验验证了此方法的有效性。      

一种有效的压缩感知图像重建算法

多数现有的压缩感知重建算法基于单观测向量,处理图像信号时将其表示成一维信号,算法效率低,重建图像的效果也有待提高.本文提出了一种基于多观测向量和稀疏贝叶斯学习的重建算法,通过同时处理观测矩阵的每一列直接求得加权系数矩阵,从而快速重建图像.在相同的采样率条件下,该算法的重建图像效果更好,算法效率明显提高.采用标准测试图像...     

An Efficient Near Lossless Image Compression Algorithm Using Dissemination of Spatial Correlation for Remote Sensing Color Images

Wireless Personal Communications - Image transmission from space borne equipment is a challenging task owing to its restrictions on bandwidth and memory requirement. Many of the state of art...     

基于压缩感知的空域信号DOA估计

研究了基于压缩感知的空域信号DOA估计问题。针对空域信号DOA算法采样数据量大,在低信噪比情况下估计结果较差的问题,文中提出一种基于奇异值分解的多矢量欠定系统聚焦求解算法(SVD-MFOCUSS)。该算法在一定程度上克服了稀疏重构算法在低信噪比情况下的缺陷,具有较低的运算复杂度。实验仿真验证了该算法性能优于传统的DOA估计算法,且能够对相干信号进行有效的DOA估计。     

An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals

Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods.      

基于交织抽取与分块压缩感知策略的图像多描述编码方法

该文基于交织抽取和分块压缩感知(Interleaving Extraction and Block Compressive Sensing,IEBCS)理论,提出了一种可以在成像过程中实时实现的多描述编码方法(IEBCS-MDC)。首先利用交织抽取将图像划分成若干个子图像,然后对各个子图像进行分块压缩感知形成多个描述码流,接收端通过求解优化问题重建原图像。分块策略保证了观测过程的复杂程度不因图像尺寸而改变,所以该方法结构简单易于实现,适合处理高分辨率图像,另外特有的自恢复能力提升了算法的抗丢包性能。实验表明,在相同的硬件环境下,该文方法可以处理的图像尺寸远远大于CS-MDC方法,在同样的丢包率下重构质量也优于CS-MDC方法。     

分块压缩感知的全变差正则化重构算法

针对分块压缩感知(BCS)重建图像质量较差问题,该文提出一种最小化l₀范数的分块压缩感知全变差(TV)正则化迭代阈值图像重构算法(BCS-TVIT)。BCS-TVIT算法考虑图像的局部平滑、有界变差等性质,将最小化l₀范数与图像的全变差TV正则项结合,构建目标函数。针对目标函数中l₀范数项和分块测量约束项无法直接优化问题,采用迭代阈值法使重构图像l₀范数最小化,并通过凸集投影保证满足约束条件,完成了目标函数的优化求解。实验表明,与基于l₀范数最小化的分块压缩感知平滑投影算法(BCS-SPL)相比,BCS-TVIT算法重构图像峰值信噪比提高2 dB,能消除BCS-SPL的“亮斑”效应,且在视觉效果上明显优于BCS-SPL算法;与最小全变差算法相比,BCS-TVIT算法重构图像峰值信噪比提升1 dB,且能降低重构时间约2个数量级。     

基于块压缩感知的SAR层析成像方法

基于压缩感知(Compressive Sensing, CS)的SAR层析成像方法(SAR Tomography, TomoSAR),虽然实现了对目标的3维重构,但对于具有结构特性的目标其重构性能较差。针对这一问题,该文提出了采用块压缩感知(Block Compressive Sensing, BCS)算法,该方法首先在CS方法基础上将具有结构特性的目标信号重构问题转化为BCS问题,然后根据目标结构特性与雷达参数的关系确定块的大小,最后对目标进行块稀疏的l1/l2范数最优化求解。相比基于CS的SAR层析成像方法,该方法更好地利用了目标的稀疏特性和结构特性,其重构精度更高、性能更优。仿真数据和Radarsat-2星载SAR实测数据的试验结果验证了该方法的有效性。      

基于提升小波变换的无损医学图像压缩

随着医学信息网络的发展,医学图像在网络中的传输对于远程医疗、教学以及患者信息的共享非常必要。在传输过程中对传输的速度和图像的质量要求很高,对图像进行压缩传送是目前有限带宽下的必然选择,而传统的压缩算法如基于离散余弦变换(DCT)的JPEG算法、基于字典技术的游程编码RLE难以满足这些要求。本文提出基于提升小波变换的无损医学图像压缩方法,并在JPEG2000标准框架下用PC机对DICOM(Digital Imaging and Communication in Medicine)标准脑部CT图像做了实现,取得满意的效果。     

A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures

In this paper we consider the l ¹-compressive sensing problem. We propose an algorithm specifically designed to take advantage of shared memory, vectorized, parallel and many-core microprocessors such as the Cell processor, new generation Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Besides its implementation is easy. We also give evidence of the efficiency of our approach and compare the algorithm on the three platforms, thus exhibiting pros and cons for each of them.     

联合非均匀采样和压缩感知的图像压缩算法

为了提高图像重构精度,改善纹理区域视觉效果,本文将压缩感知理论与图像压缩相结合,并提出了一种新的采样方法:在编码端对图像高频部分边缘点进行密集采样,对非边缘部分进行随机抽样,取代了传统压缩感知理论中直接使用测量矩阵获得低维观测值的过程。在解码端利用采样点位置信息构造块测量矩阵,使用光滑l0范数（Smoothed l0,SL0）重构算法实现重叠块重构,最终将其与图像低频部分下采样点插值放大结果合并实现高精度重构。实验结果表明:本文算法不仅可以提高整幅图像和纹理区域的重构精度,而且在低采样率或图像尺寸较小的情况下,算法效率也有明显提升。      

Low-Delay Parallel Architecture for Fractal Image Compression

This paper presents an efficient hardware architecture for implementing fractal image compression (FIC) algorithm aimed toward image compression with improved encoding speed. The proposed architecture follows the full-search-based FIC scheme. Parallel processing has been effectively used in the present work to achieve the goal of reducing the time complexity of the encoder. This architecture requires a total of \(2n+2\) clock cycles for executing the set of operations consisting of fetching the pixels, calculating the mean of range and domain blocks and doing their mapping, computing the error, and storing the fractal parameter in a memory with n number of pixels in the range block. Further, this architecture does not make use of any preprocessing operations as specified in literature and utilizes the benefits of isometric transformation without requiring additional cycles for every single matching operation. Effective application of isometric transformation has also led to memory reduction of nearly 67 %. Again, in the present work, the use of multipliers has been avoided to save the chip area, to reduce hardware complexity, and to enhance the encoding speed. The operation of transforming contracted domain block with a zero-mean domain block has facilitated relatively fast convergence at the decoder. PSNR above 30dB for a range block of size \(4\times 4\) has been achieved by the proposed architecture, which is comparable to that realizable by other architectures. The proposed design has been coded in Verilog HDL, has been implemented in Xilinx Virtex-5 FPGA, and operates at a clock frequency of 75.52 MHz.     

基于二维压缩感知和分层特征的图像检索算法

为了保留图像分析时的像素点位置关系及降维处理,把一维压缩感知理论推广到二维,建立了二维可稀疏信号的压缩测量模型,研究了一种二维信号的自适应梯度下降重构AGDR(Adaptive Gradient Descent Recursion)算法,由此提出了一种图像分层特征提取与检索方法.首先对图像在RGB颜色空间上进行网格离散划分,通过分层算子对图像进行分层映射,定义一种基于颜色网格空间的扩展灰度共生矩阵,采用二维测量模型获取图像的分层测量特征、纹理特征与分层颜色统计特征,图像分层测量特征综合反映出图像的颜色及像素点位置的关系,扩展灰度共生矩阵反映纹理特征.其次用AGDR算法计算检索图像之间的原始信号差量及其稀疏值.最后结合两类分层特征差量、稀疏值和颜色统计特征,融合计算图像间整体相似度度量指标.仿真实验表明,应用分层二维压缩感知测量与AGDR算法的图像检索方法在检索时间、查全率和查准率等指标上具有优越性能,为图像检索提供了新思路.     

Image Compression Using Block Truncation Coding

A new technique for image compression called Block Truncation Coding (BTC) is presented and compared with transform and other techniques. The BTC algorithm uses a two-level (one-bit) nonparametric quantizer that adapts to local properties of the image. The quantizer that shows great promise is one which preserves the local sample moments. This quantizer produces good quality images that appear to be enhanced at data rates of 1.5 bits/picture element. No large data storage is required, and the computation is small. The quantizer is compared with standard (minimum mean-square error and mean absolute error) one-bit quantizers. Modifications of the basic BTC algorithm are discussed along with the performance of BTC in the presence of channel errors.     

基于Gabor变换的一种有效的图像水印技术

在大量实验的基础上,提出了一种基于离散Gabor变换（DGT）的强数字水印实现技术.将数字水印信息隐藏在DGT的低频系数中,在水印的检测算法中采用了几何变换及与其相配套的匹配技术,将有几何形变的图像反变换恢复,再通过DGT进行检测.所提出的方法采用综合性水印测试软件StirMark 4.0进行了大量的模拟攻击实验,在几乎所有的实验中检测算法都成功地检测到隐含于数字图像数据中的水印信息,表明所提出方法具有很好的鲁棒性及高效性能.