一种基于双正交小波变换和格型矢量量化的视频编码算法

本文提出了基于双正交小波变换（ｂｉｏｒｔｈｏｇｏｎａｌｗａｖｅｌｅｔｔｒａｎｓｆｏｒｍ）和格型矢量量化（ＬＶＱｌａｔｔｉｃｅｖｅｃｔｏｒｑｕａｎｔｉｚａｔｉｏｎ）的视频编码算法。在该方案中，小波变换将图像分解成多分辨率（ｍｕｌｔｉｒｅｓｏｌｕｔｉｏｎ）的子带图像，多分辨率运动估值（ＭＲＭＥ，ｍｕｌｔｉｒｅｓｏｌｕｔｉｏｎｍｏｔｉｏｎｅｓｔｉｍａｔｉｏｎ）技术实现子带图像的帧间预测，格型矢量量化对预测差值子带图像进行编码，从而获得了性能较好的活动图像编码新算法。     

基于动态小波神经网络非线性预测的语音编码系统

现在,所有的语音编码系统都采用线性预测技术,但对于本质非线性的语音信号而言,线性预测是不够的.因此,本文提出一种带反馈单元的动态小波神经网络并将其应用于语音编码系统,并对其函数逼近能力和学习高维函数的优越性进行分析.由于反馈单元的内部记忆能力,动态神经网络具有对长时相关的预测能力并能在一定程度上克服小波神经网络的"维数灾难"问题;在对语音信号的预测中,动态小波神经网络预测器的预测性能很好,虽然其预测阶数很低(仅为2).由于预测器较好的预测性能,当将此预测器用于语音编码系统中的后向预测时,实验结果表明:新系统的恢复语音平均分段信噪比比ITU的G.721标准提高3～4dB但二者码率相同.另外非线性预测语音编码系统的计算量是可以接受的.     

基于整型小波变换的遥感图像压缩

对给定的遥感图像采用基于“提升”（Lifing)算法的整型小波变换，然后对变换后的小波域图像进行编码。在小波域编码时，采用逐次近似量化和LZ77方法编码。当量化门限较高时，则有用EZW方法直接对小波域上的图像进行编码，当门限较低时，则对EZW编码后的残差信息进行LZ77编码，文章用上述方法实现了遥感图像的无损压缩，提高了图像的压缩效果。     

一种基于回归神经网络的码本激励非线性预测话音编码算法

为改善预测类声码器中长时预测器特性，本文引入了一种全连接回归神经网络（ＦＲＮＮ）非线性预测器并将其应用于话音编码算法中。ＦＲＮＮ在隐层单元不仅有来自自身的反馈，也有来自输出单元的反馈，因此其预测性能好于常规预测器。将其应用于码本激励话音编码系统（ＣＥＬＰ）中，可得到较低的传输码率，同时亦可改善编码质量。     

图像多小波域矢量量化策略

提出一种基于多小波变换结合矢量量化的图像编码算法(MDWT VQ)。首先对图像进行多小波分解,然后对高频系数用改进后的LBG算法形成的码书进行VQ编码。算法充分利用了多小波域不同分辨率层间各方向子图像的相似性,仅对最高分辨率层进行码书地址索引,低级分辨率层的系数按照一定的组织形式直接套用最高分辨率层的地址索引信息。对比实验的结果验证了该算法在提高图像的重建质量以及在降低位码率方面均比传统的单小波图像编码算法有一定的提高。     

小波变换在实时联合变换相关器中应用的研究

通过目标图像的小波变换获得图像的多分辨率小波梯度信息,再利用线性或非线性的变换来处理图像的小波梯度信息,这样便得到了一幅梯度信息增强后的小波系数,利用图像复原技术便能得到对比度增强后的目标图像,从而使实时联合变换相关器能较好地识别复杂背景低对比度的目标图像.     

基于小波变换及神经网络的SAR图像目标检测方法研究

由于小波分解的多分辨分析特征及神经网络的自学习、自组织等性能,在图像处理中得到了广泛的应用。本文研究了SAR图像非线性采样目标低频小波树特征提取方法,利用PCA（主分量分析）对低频小波树降维,用降维后的特征值训练LVQ神经网络,将其应用于SAR图像目标检测,取得了较好的检测结果。     

基于背景杂波自适应预测的红外弱小目标检测

文章根据背景杂波和运动目标的红外成像差异,提出了两种自适应杂波预测技术的低 信噪比小目标检测方法。首先讨论了一种基于均方误差最小的自适应线性预测器,该预测器计算量小,易满足实时要求,且对平稳和线性云层红外背景图像具有很好的背景预测能力。然后提出了一种基于非线性函数可调整的BP 神经网络预测器,该预测器中的非线性函数可调整且非线性程度很高,能很好的适应各种复杂的起伏背景,特别是非平稳和非线性杂波背景。文中还通过实际的红外图像验证了两种方法的有效性。     

图像小波域视觉重要性顺序模型比特零树预测熵编码

本文研究一种静态图像小波（Ｗａｖｅｌｅｔ）域系数压缩方法，可以在任意给定比特数时获得一定视觉意义下的最佳图像质量。该方法根据图像小波分解和人类视觉（ＨＶＳ）的特点及其关系，对系数进行不同间隔的量化，并规定系数的视觉重要性顺序，结合零树数据结构，对系数进行该顺序的比特层零树扫描和预测，输出符号数据流，最后用自适应算术熵编码实现高效率编码。该方法易于用ＶＬＳＩ实现，输出比特率任意可调。计算机模拟结果显示，对于图像（Ｌｅｎａ２５６）在０．２～０．３ｂｉｔ／ｐｉｘｅｌ时仍可获得较满意的重构图像质量     

基于嵌入零树小波算法的磁共振图像压缩研究

医学影像技术的广泛应用，产生了大量图像数据，医学图像压缩技术可以在保证图像使用质量的前提下，将图像的数据量减小，以便于存储和传输．医学图像压缩技术很多，小波分析以其多分辨率分析特性，能很好的满足人眼的视觉特性，近年得到迅猛的发展，其中嵌入零树小波（Embedded Zerotree Wavelet，EZW）算法又堪称其中的经典。但该算法编码时间长，效率不高，没有充分利用小波的多分辨率分析特性。文中在分析EZW算法基础上，将游程长度编码（Run Length Coding RLC）溶入其中，提出一种改进的医学图像压缩技术。     

S-transform-based intelligent system for classification of power quality disturbance signals

In this paper, a new approach is presented for the detection and classification of nonstationary signals in power networks by combining the S-transform and neural networks. The S-transform provides frequency-dependent resolution that simultaneously localizes the real and imaginary spectra. The S-transform is similar to the wavelet transform but with a phase correction. This property is used to obtain useful features of the nonstationary signals that make the pattern recognition much simpler in comparison to the wavelet multiresolution analysis. Two neural network configurations are trained with features from the S-transform for recognizing the waveform class. The classification accuracy for a variety of power network disturbance signals for both types of neural networks is shown and is found to be a significant improvement over multiresolution wavelet analysis with multiple neural networks.     

一种新的二维非线性提升小波变换方法

根据图像的统计信息,本文构造了一种新的非线性算子即统计算子,提出了基于该算子的一种新的非线性提升小波分析方法.使图像经过该方法变换以后,在无量化失真的前提下,以较大概率取得零高频系数.本文将该方法与现存文献中所提出的非线性形态学小波等分析方法,进行了标准图像的测试分析,实验结果显示,利用本文所提出的基于统计算子的提升小波分析的方法所得到的高频子带的熵都低于其它几种非线性小波变换,取得了很好的分析结果.     

一种适于计算声场景分析的混叠语音基音检测方法

本文提出了一种在混叠语音信号中检测各自语音分量基音信息的方法.该方法采用小波变换作为基音检测模型中的滤波处理,并用广义自相关运算突出基音信息,用增强自相关累和消除冗余信息,并提出了用基音概率函数来预测并跟踪不同基音的变化以提高基音检测的准确性.本文提出的方法可应用于计算声场景分析中.实验结果表明,该方法对于混叠语音的基音检测是非常有效的.     

Neural network architectures for vector prediction

A vector predictor is an integral part of a predictive vector quantization coding scheme. The conventional techniques for designing a nonlinear predictor are extremely complex and suboptimal due to the absence of a suitable model for the source data. We investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron, the functional link network and the radial basis function network. We also evaluated and compared the performance of these neural network predictors with that of a linear vector predictor. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor. However, the performance of a neural network predictor is comparable to that of a linear predictor for predicting the stationary and shade blocks     

Solution of inverse problems in image processing by waveletexpansion

We describe a wavelet-based approach to linear inverse problems in image processing. In this approach, both the images and the linear operator to be inverted are represented by wavelet expansions, leading to a multiresolution sparse matrix representation of the inverse problem. The constraints for a regularized solution are enforced through wavelet expansion coefficients. A unique feature of the wavelet approach is a general and consistent scheme for representing an operator in different resolutions, an important problem in multigrid/multiresolution processing. This and the sparseness of the representation induce a multigrid algorithm. The proposed approach was tested on image restoration problems and produced good results.     

Motion compensated lossy-to-lossless compression of 4-D medical images using integer wavelet transforms.

This paper proposes a method for progressive lossy-to-lossless compression of four-dimensional (4-D) medical images (sequences of volumetric images over time) by using a combination of three-dimensional (3-D) integer wavelet transform (IWT) and 3-D motion compensation. A 3-D extension of the set-partitioning in hierarchical trees (SPIHT) algorithm is employed for coding the wavelet coefficients. To effectively exploit the redundancy between consecutive 3-D images, the concepts of key and residual frames from video coding is used. A fast 3-D cube matching algorithm is employed to do motion estimation. The key and the residual volumes are then coded using 3-D IWT and the modified 3-D SPIHT. The experimental results presented in this paper show that our proposed compression scheme achieves better lossy and lossless compression performance on 4-D medical images when compared with JPEG-2000 and volumetric compression based on 3-D SPIHT.     

Wavelet coding of volumetric medical images for high throughput and operability

This paper presents a new three-dimensional (3-D) wavelet-based scalable lossless coding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3-D dyadic wavelet transform tailored to volumetric medical images and an optimized Rice code of very low complexity.     

Nonlinear prediction in image coding with DPCM

In contrast to the traditional linear differential pulse code modulation (DPCM) design for the encoding of images, a new, nonlinear, neural network-based, DPCM technique has been devised. The predictor is designed by supervised training, based on a typical sequence of pixel values in an image. A function link neural network architecture has been used to design the predictor for one dimensional (1-D) DPCM. Computer simulation experiments in still image coding have shown that the resulting encoders work very well. At a transmission rate of 1 bit/pixel, for the image LENA, the 1-D neural network DPCM provides a 4.2 dB improvement in SNR over the standard linear DPCM system.<>     

Multiscale video representation using multiresolution motioncompensation and wavelet decomposition

A multiscale video representation using wavelet decomposition and variable-block-size multiresolution motion estimation (MRME) is presented. The multiresolution/multifrequency nature of the discrete wavelet transform makes it an ideal tool for representing video sources with different resolutions and scan formats. The proposed variable-block-size MRME scheme utilizes motion correlation among different scaled subbands and adapts to their importance at different layers. The algorithm is well suited for interframe HDTV coding applications and facilitates conversions and interactions between different video coding standards. Four scenarios for the proposed motion-compensated coding schemes are compared. A pel-recursive motion estimation scheme is implemented in a multiresolution form. The proposed approach appears suitable for the broadcast environment where various standards may coexist simultaneously     

Adaptive prediction for lossless image compression

Lossless image compression is often performed through decorrelation, context modelling and entropy coding of the prediction error. This paper aims to identify the potential improvements to compression performance through improved decorrelation. Two adaptive prediction schemes are presented that aim to provide the highest possible decorrelation of the prediction error data. Consequently, complexity is overlooked and a high degree of adaptivity is sought. The adaptation of the respective predictor coefficients is based on training of the predictors in a local causal area adjacent to the pixel to be predicted. The causal nature of the training means no transmission overhead is required and also enables lossless coding of the images.The first scheme is an adaptive neural network, trained on the actual data being coded enabling continuous updates of the network weights. This results in a highly adaptive predictor, with localised optimisation based on stochastic gradient learning. Training for the second scheme is based on the recursive LMS (RLMS) algorithm incorporating feedback of the prediction error. In addition to the adaptive prediction, the results presented here also incorporate an arithmetic coding scheme, producing results which are better than CALIC.