基于小波变换的低位率图像量化编码

提出一种运用非均匀量化的思想对小波低频子带系数进行量化编码的算法.首先逐级对小波低频系数的能量分布进行判别,然后根据小波系数的幅值特性自适应地对最低频小波系数采用较大的量化步长进行量化编码,而对其余的高频小波系数采用较小的量化步长.试验结果表明,在低位率编码时提高了编码性能.     

基于小波的数字高程模型压缩方法

根据地理信息系统中数字高程模型（DEM）的特点，提出一种基于小波变换的DEM数据压缩方法．文中主要研究了小波滤波器的选取、最优标量量化器的设计等问题，并对DEM小波分解系数进行了深入的分析．同时，应用该方法对实验DEM数据进行测试．结果表明，该方法较其他方法更为有效，能够在取得大压缩比的同时，有较好的数据恢复质量．     

基于EZW的ECG压缩算法研究与改进

嵌入零树小波(Embeded Zerotree Wavelet,EZW)作为一种高效的压缩方式,虽然在编码上具有一定的优越性,但是其多层零树结构信息编码会使信号的压缩比有所降低。因此,研究了EZW压缩算法的优化与改进。首先研究了使用提升小波格式处理ECG信号,其中重点研究了小波的消失矩提升规律与提升算法;其次对EZW压缩算法编码方式的改进进行了研究。对ECG分解信息进行特征值检测,然后根据特征信息将ECG的小波系数进行加权计算。通过衡量系数的权重,再进行优化编码,从而达到了改进压缩效益的目标。     

基于小波变换和优化的SVM的网络流量预测模型

提出一种基于小波变换和优化的SVM的网络流量预测模型(WaOSVM),首先对网络流量进行无抽取小波分解得到小波系数和尺度系数,然后选取适当核函数的SVM分别进行预测,其中SVM的参数用自适应量子粒子群算法(AQPSO)进行优化,最后将各预测结果进行小波重构得到最终预测结果.实验结果表明:优化过的SVM具有较好的泛化能力...     

基于提升小波和LZW的机械振动信号数据压缩

针对机械状态监测系统中基于CAN总线的传感器网络数据传输量大且浮点型测试数据难以直接进行压缩的问题,提出一种基于提升小波分析和LZW的二级机械振动信号数据压缩方法.首先,利用提升小波对测试数据进行小波域稀疏分解,利用分块阈值对小波系数进行阈值估计和量化,去除噪声干扰,突出有用信息,实现初级数据压缩;其次,针对浮点型小波系数的数据压缩问题,提出了一种基于LZW的分裂并行量化编码方法,在不丢失数据信息的同时,进一步提高了数据的可压缩性能;最后,齿轮箱实测振动信号的数据分析结果表明了所提数据压缩方法的有效性.     

基于DWT域对数量化索引调制的数字水印算法

为了提高水印的透明性和鲁棒性,解决QIM均匀量化不适用于非均匀信号的问题。引入μ律技术,将小波分解与对数量化索引调制结合,提出基于DWT域的对数量化索引调制数字水印算法。算法对原始图像进行离散小波变换,对变换后的近似部分系数运用μ律函数,再用QIM方法嵌入二值水印。选择合适的嵌入位置,通过对量化步长和参数进行讨论得到其最优值。仿真实验结果表明,该算法与以前的量化算法相比提高了水印的透明性,并对高斯噪声、中值滤波、JPEG压缩等常见攻击具有更强的鲁棒性。     

基于非均匀量化的小波域分布式深度图视频编码

针对分布式多视点加深度格式(DMVD)的视频编码中深度图视频解码质量问题,提出一种结合子带层及子带系数的小波域分布式深度视频非均匀量化方案,通过给边缘分配更多比特来提升深度图的边缘质量。结合深度图经小波变换后系数分布特性,对第N层的低频小波系数采用均匀量化方案,对其他层高频小波系数采用非均匀量化方案。针对高频系数的非均匀量化,对处于"0"左右的高频系数采用较大的量化步长,随着高频系数幅度值的增大,量化步长逐渐减小,量化逐渐精细,从而提升深度图中的边缘细节质量。实验结果表明,对于边缘较多且变化较明显的"Dancer"和"PoznanHall2"深度序列,该算法能够有效地提高二者的边缘信息质量从而提高其率失真(R-D)性能,最高可达1.2 dB;而对于边缘区域较小且较为模糊的"Newspaper"和"Balloons"深度序列,系统的R-D性能也能被提升0.3 dB左右。     

基于db4小波变换的电能质量数据压缩算法

针对传统小波变换运用于数据压缩算法存在的问题,提出了基于db4小波变换的数据压缩算法。该算法针对电能质量数据的特点,根据db4小波变换具有多分辨率分析的特性,将检测数据变换到小波域,以使信号能量主要集中于低频系数上,而后采用软阈值压缩算法处理小波低频系数。并采用游程编码的方式存储数据。对实际数据进行压缩,该算法压缩效果良好,计算速度快,适用于实时性要求较高的场合。     

一种增强的LPC参数多级矢量量化技术

为充分利用码本的级间相关性,提出了一种联合码本优化多级矢量量化(JCO-MSVQ)码本设计方法。每次迭代时,先将训练矢量对码字进行聚类,再对各级码本进行联合优化,利用条件期望逐级更新码本。实验数据表明,该算法在设计10维线谱频率(LSF)参数量化码本时,较随机松弛算法(SR)码本有更小的平均量化畸变。23比特/帧LSF参数量化器平均对数谱失真为0.87dB,达到了透明量化要求。     

一种基于小波变换的图像压缩方法

提出一种基于小波变换的灰度图像数据压缩编码方法,基本思路是利用小波变换实现图像的多分辨分解,用矢量量化（ＶＱ）对分解后的图像进行编码,在矢量量化ＬＢＧ算法的初始码书的选取中根据矢量中各分量的特性提出一种改进的随机选取法,避免了可能的胞腔不均现象,提高了码书的质量,而且重构的图像质量也有所提高。     

Application of the Max-Lloyd quantizer for ECG compression in diving mammals

This article presents a practical implementation of an ECG compression algorithm using a Max-Lloyd quantizer, to optimize the low resources of an ECG acquisition and transmission system (telemetry system) for dolphins and human divers. The algorithm scheme is based on a first-order differential pulse code modulation (DPCM) and uses a Max-Lloyd quantizer to code the difference between the current and predicted samples. The use of the non-uniform quantizer instead of a uniform quantizer improves the percent root mean-square difference (PRD), thereby producing a low distortion in the reconstructed signals. Due to its low computational complexity, the compression process can be accomplished on-line during the ECG acquisition process.     

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。     

Efficient transform-based texture image retrieval techniques under quantization effects

With the great demand for storing and transmitting images as well as their managing, the retrieval of compressed images is a field of intensive research. While most of the works have been devoted to the case of losslessly encoded images (by extracting features from the unquantized transform coefficients), new studies have shown that lossy compression has a negative impact on the performance of conventional retrieval systems. In this work, we investigate three different quantization schemes and propose for each one an efficient retrieval approach. More precisely, the uniform quantizer, the moment preserving quantizer and the distribution preserving quantizer are considered. The inherent properties of each quantizer are then exploited to design an efficient retrieval strategy, and hence, to reduce the drop of retrieval performances resulting from the quantization effect. Experimental results, carried out on three standard texture databases and a color dataset, show the benefits which can be drawn from the proposed retrieval approaches.     

基于Contourlet的图像压缩算法

Contourlet变换结合了方向滤波组,具备小渡变换不能表达的多方向性,能够很有效地捕获自然图像的边缘轮廓信息。在JPEG2000的压缩标准中,它采用了小渡变换和死区均匀量化。鉴于JPEG2000标准,本文提出一套新的编码方案——contoludet变换和最佳量化器（Lloyd—Max量化器）。同样,本文也将死区量化应用到最佳量化器中。     

Unsupervised learning approach to adaptive differential pulse code modulation

This research is concerned with investigating the problem of data compression utilizing an unsupervised estimation algorithm. This extends previous work utilizing a hybrid source coder which combines an orthogonal transformation with differential pulse code modulation (DPCM). The data compression is achieved in the DPCM loop, and it is the quantizer of this scheme which is approached from an unsupervised learning procedure. The distribution defining the quantizer is represented as a set of separable Laplacian mixture densities for two-dimensional images. The condition of identifiability is shown for the Laplacian case and a decision directed estimate of both the active distribution parameters and the mixing parameters are discussed in view of a Bayesian structure. The decision directed estimators, although not optimum, provide a realizable structure for estimating the parameters which define a distribution which has become active. These parameters are then used to scale the optimum (in the mean square error sense) Laplacian quantizer. The decision criteria is modified to prevent convergence to a single distribution which in effect is the default condition for a variance estimator. This investigation was applied to a test image and the resulting data demonstrate improvement over other techniques using fixed bit assignments and ideal channel conditions.     

多个体切换网络分布式量化次梯度优化算法

由于已有的分布式次梯度算法大多基于理想的假设：网络拓扑是有向平衡的,构成网络的个体间通信的是各个个体某个状态变量的完全精确的信息。针对更一般的非平衡切换网络以及实际生活中网络通道的带宽限制,提出一种基于有限量化信息通信的切换网络分布式量化次梯度优化算法。在非平衡切换网络中,通过设计具有有限量化水平的一致量化器使所有信息在发送之前都经过量化,利用非二次李雅普诺夫函数方法,证明了所提出的多个体分布式量化次梯度优化算法的收敛性。最后仿真实例验证了所提算法的有效性,而且通过调节量化水平参数,在相同的带宽条件下,可提高信息传输速率,使网络中的个体更快地达到一致。该方法弱化了对刻画网络拓扑的邻接矩阵的假设及对网络带宽的要求,更具实用性。     

Synthesis of Optimal Dynamic Quantizers for Discrete-Valued Input Control

This paper presents an optimal dynamic quantizer synthesis method for controlling linear time-invariant systems with discrete-valued input. The quantizers considered here include dynamic feedback mechanism, for which we find quantizer parameters such that the system composed of a given linear plant and the quantizer is an optimal approximation of the linear plant in terms of the input-output relation. First, the performance of an arbitrarily given dynamic quantizer is analyzed, where we derive a closed form expression of the performance. Based on this result, it is shown that the quantizer design is reduced to a nonconvex optimization problem for which it is hard to obtain a solution in a direct way. We obtain a globally optimal solution, however, by taking advantage of a special structure of the problem which allows us to relax the original nonconvex problem. The resulting problem is easy to solve, so we provide a design method based on linear programming and derive an optimal structure of the dynamic quantizers. Finally, the validity of the proposed method is demonstrated by numerical examples.     

传感信号采集中的误差受控压缩与压缩上限的确定

提出了一种传感信号采集中的误差受控压缩算法.为适应传感信号特征多变的情况,根据各段信号的自相关系数动态调整梯度预测器的系数;通过改进最大步长均匀量化器降低量化噪声;采用Golomb-Rice编码算法对量化后的预测误差序列进行编码.根据数据采集系统前端噪声水平确定压缩误差参数的上限,进而获得压缩比的上限.算法在供水管道泄漏信号采集中的应用表明,压缩比达2.63时,压缩后重构信号漏点定位误差增加量小于0.2 m.     

Combining image compression and classification using vectorquantization

We describe a method of combining classification and compression into a single vector quantizer by incorporating a Bayes risk term into the distortion measure used in the quantizer design algorithm. Once trained, the quantizer can operate to minimize the Bayes risk weighted distortion measure if there is a model providing the required posterior probabilities, or it can operate in a suboptimal fashion by minimizing the squared error only. Comparisons are made with other vector quantizer based classifiers, including the independent design of quantization and minimum Bayes risk classification and Kohonen's LVQ. A variety of examples demonstrate that the proposed method can provide classification ability close to or superior to learning VQ while simultaneously providing superior compression performance     

Optimization of multiple region quantizer for Laplacian source

This paper proposes a multiple region quantizer composed of quantizers defined on different disjunctive regions of an input signal. In particular, for the two region and the three region cases, the paper provides a complete optimization of a multiple region companded quantizer for the Laplacian source of unit variance. The analysis of the multiple region quantizer is limited to a three region case due to the complexity of the optimization problem and due to the fact that much more complex multiple region quantizer models obtained for a higher number of regions could slightly improve the performances. Two-stage optimization is performed with respect to the number of reconstruction levels of each quantizer composing the considered multiple region companded quantizer and with respect to the region bounds. It is shown that optimal parameters depend only on the fractional part of the required average bit rate. In order to design the three region optimal quantizer, Lloyd–Max's algorithm and Newton–Kantorovich iterative method are used with the three region optimal companded quantizer as the initial solution. The gradient Newton–Kantorovich iterative method is used to provide better convergence speed than Lloyd–Max's algorithm, which is essential in cases where effective initialization solution of Lloyd–Max's algorithm is missing. It is shown that the three region optimal companded quantizer have signal to quantization noise ratio value close to the one of the three region optimal quantizer, where a simpler design procedure is the benefit of the three region optimal companded quantizer over the three region optimal one.