Lattice Vector Quantization Applied to Speech and Audio Coding

Lattice vector quantization （LVQ） has been used for real-time speech and audio coding systems. Compared with conventional vector quantization, LVQ has two main advantages： It has a simple and fast encoding process, and it significantly reduces the amount of memory required. Therefore, LVQ is suitable for use in low-complexity speech and audio coding. In this paper, we describe the basic concepts of LVQ and its advantages over conventional vector quantization. We also describe some LVQ techniques that have been used in speech and audio coding standards of international standards developing organizations （SDOs）.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.

Speech compression scheme based on wavelet transform and vector quantization

A coding method of speech compression, which is based on Wavlet Transform and Vector Quantization (VQ), is developed and studied. The Wavlet Thansform or Wavlet Packet Thansform is used to process the speech signal, then VQ is used to compress the coefficients of Wavlet Thansform, and the entropy coding is used to decrease the bit rate. The experimental results show that the speech signal, sampled by 8 kHz sampling rate and 8 bit quatisation,i.e., 64 kbit/s bit rate, can be compressed to 6 - 8 kbit/s, and still have high speech quality,and the low-delay, only 8 ms.

Fast encoding algorithm for vector quantization based on subvector L_2-norm

A fast encoding algorithm based on the mean square error （MSE） distortion for vector quantization is introduced. The vector, which is effectively constructed with wavelet transform （WT） coefficients of images, can simplify the realization of the non-linear interpolated vector quantization （NLIVQ） technique and make the partial distance search （PDS） algorithm more efficient. Utilizing the relationship of vector L2-norm and its Euclidean distance, some conditions of eliminating unnecessary codewords are obtained. Further, using inequality constructed by the subvector L2-norm, more unnecessary codewords are eliminated. During the search process for code, mostly unlikely codewords can be rejected by the proposed algorithm combined with the non-linear interpolated vector quantization technique and the partial distance search technique. The experimental results show that the reduction of computation is outstanding in the encoding time and complexity against the full search method.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Image Coding Using Wavelet Transform and EntropyConstrained Vector Quantization with Quadtree Structure Vectors

This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional （1D） linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional （2D） integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ （WSFCVQ）, the method of improved BiBlock zero tree coding （IBBZTC） and the method of feature spectral vector quantization （FSVQ）, the peak signal-to-noise ratio （PSNR） of this method can enhance over 9 dB, and the total compression performance is improved greatly.

基于图像伪装与双向差值扩展的密文域可逆信息隐藏算法

数字图像在云环境下的安全性问题已成为信息安全领域的研究热点之一。为解决传统密文域可逆信息隐藏算法缺乏伪装性,容易受到恶意攻击与信息隐藏空间容量小的问题,提出了基于图像伪装加密与双向差值扩展的大容量密文域可逆信息隐藏算法。该算法首先利用矢量量化与离散小波变换两种技术对原始图像进行伪装加密,从而保证隐蔽性,使得图像在云环境中安全传递;其次采用基于双向差值扩展的信息隐藏技术对伪装加密图像进行秘密信息的嵌入,以实现高容量的信息隐藏。实验结果表明,所提算法不仅实现了图像伪装,而且最终得到的含密伪装图像与原始图像在视觉上无明显差别,峰值信噪比达到40 dB以上,对原始图像起到了很好的伪装效果;同时实现了高容量的秘密信息嵌入,图像平均嵌入率接近0.6 bpp,表现出了良好的实验性能。

基于流量行为特征的网络异常稳定识别仿真

异常行为识别是保证网络安全运行不可缺少的步骤,但异常行为识别过程易受噪声流量、网络性能、冗余信号的干扰。为了解决上述问题,提出基于流量行为特征的网络异常行为识别方法。采用提升小波变换法剔除网络流量中的噪声,避免识别过程受到噪声干扰,采用矢量量化技术与主题模型提取流量行为特征,将提取的特征输入到支持向量机模型中,利用支持向量机的二分类特性实现特征的分类,完成网络异常行为的识别。实验结果表明,所提方法的异常识别精度高,且AUC-PR数值比较稳定,说明其在识别过程中不受噪声数据的影响。

A New Quality Improving Scheme for VQ Decompressed Images Based on DWT

The better compression rate can be achieved by the traditional vector quantization （VQ） method, and the quality of the recovered image can also be accepted. But the decompressed image quality can not be promoted efficiently, so how to balance the image compression rate and image recovering quality is an important issue, in this paper, an image is transformed by discrete wavelet transform （DWT） to generate its DWT transformed image which can be compressed by the VQ method further. Besides, we compute the values between the DWT transformed image and decompressed DWT transformed image as the difference matrix which is the adjustable basis of the decompressed image quality. By controlling the deviation of the difference matrix, there can be nearly Iossless compression for the VQ method. Experimental results show that when the number of compressed bits by our method is equal to the number of those bits compressed by the VQ method, the quality of our recovered image is better. Moreover, the proposed method has more compression capability comparing with the VQ scheme.

基于小波变换的有损图像压缩算法研究

文章从实际应用角度出发，全面系统地阐述了小波变换基本理论，提出了一种基于小波变换的有损图像压缩算法。该算法首先利用小波变换对图像进行分解，然后根据各子图像特点对小波系数进行相应的向量量化编码，从而实现图像压缩目的。实验结果表明：该图像压缩算法的使用，可以在图像质量几乎不下降的条件下，极大地提高压缩比，改善恢复图像质量。

7/5小波滤波器簇及其图像压缩性能研究

构造了包括BT7/5滤波器(Brislawn-Treiber)在内的7/5小波滤波器簇,依据欧几里得算法给出了7/5小波基提升格式的构造过程和提升实现算法.根据7/5小波基优化条件的要求,证明了7/5小波基的、提升系数α为0.08时的图像压缩性能最佳,该小波基为JPEG2000标准第二部分推荐使用的BT7/5小波滤波器组.BT7/5小波的系数为有理数,与无理系数的CDF9/7小波滤波器相比,具有计算复杂度低和便于VLSI硬件实现等优点.实验结果表明,若压缩比在100∶1以内,采用BT7/5小波滤波器的重构图像峰值信噪比只比CDF9/7低0.1dB,而比LT5/3(LeGall-Tabatabai)高1.2dB.因此,在JPEG2000图像压缩系统中,BT7/5是LT5/3和CDF9/7的理想替代者.

一种基于小波变换的图像压缩方法

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率分解 ,用矢量量化 (VQ)对分解后的图像进行编码 ,在矢量量化 L BG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。

基于肌电信号的人手运动状态的辨识

研究的目的在于利用人体前臂的肌电信号进行人手动作模式的识别。根据采集的肌电信号,判断动作始末状态并对该肌电信号进行小波降噪预处理,利用小波变换的高频细节系数极值构造特征矢量,经过学习矢量量化(LVQ)神经网络训练,能够有效地识别握拳、展拳、手腕内旋和手腕外旋4种动作模式。和前馈型神经网络比较,LVQ神经网络具有更高的识别准确率和更稳定的再现性。

基于小波变换和压缩感知的低速率语音编码方案

本文提出一种新的低速率语音编码方案,基于语音信号小波变换高频系数的稀疏性,利用压缩感知原理,将小波变换高频系数进行压缩感知投影成数据量大大减少的观测序列,然后对观测序列采用码激励线性预测技术进行编解码,根据解码后的观测序列,利用线性规划技术对小波变换高频系数进行重构,小波变换低频系数采用矢量量化技术编解码,并采用后置低通滤波器改善解码后小波高低频系数合成语音的听觉效果。该编码方案在低数码率(2.64～3.5 Kb/s)时得到的重构语音平均MOS分为3.0～3.4,达到4.8 Kb/s码激励线性预测语音编码质量。

基于小波变换及矢量量化的隐像术

提出了一种新的图像保密系统 (称为隐像术 ) ,它把有关秘密图像的信息加密隐藏在另一可公开传送的普通图像中 ,不易引起窃听者的注意 ,达到了保护秘密图像的目的 .试验结果表明 :恢复出的秘密图像的 PSN R值可达 2 2 .83d B,证明了隐像术有着令人满意的恢复质量 .隐像术的优点是 :用可公开传送的普通图像隐藏秘密图像 ,不易为窃听者所察觉 ;有效地压缩了所需保密的数据 ,一幅可公开传送的普通图像中可隐藏数幅秘密图像 ;秘密图像的信息是用 DES算法加密的 。

基于小波包变换和矢量量化的电力系统故障数据压缩

在实际的故障分析中通常不是利用故障暂态信号的全部频带信息 ,而是利用其部分频带信息来解决问题 ,因此提出了针对电力系统故障信号压缩的特殊要求 ,即对于所要解决问题有重要价值的频带信息无损压缩 ,对不重要的频带信息可以失真压缩 ,即保频带压缩。在此基础上提出了基于小波包变换、Huffman编码方法和矢量量化相结合的故障数据压缩算法 ,首先利用小波包变换将故障信号分解成各个频带的信息 ,选择有重要信息的频带采用自适应 Huffman无损压缩算法 ,而对于其他频带数据信息 ,采用矢量量化压缩算法。通过仿真测试 ,该算法较好地满足了故障暂态信号的压缩要求 。

压缩域图像处理技术研究

压缩格式作为多媒体数据的存储和传输形式具有普遍性，而实际的多媒体应用还需要对多媒体数据进行各种灵活的处理和操作．传统的数据处理是在原始数据域进行的，对于普遍存在的压缩形式的多媒体数据，必须先解压缩才能进行相应的处理，这极大地影响了多媒体应用的整体性能和响应速度．在这样的背景下，压缩域多媒体数据的直接处理技术的研究得到了广泛的重视和发展．文中主要从基本概念、研究内容和发展现状等方面对图像数据的压缩域处理技术进行了综述，并对这一领域的发展前景进行了一些有益的探讨．

小波变换在图象压缩中的应用

文章从实际应用角度出发，提出了一种基于小波变换和改进 LBG向量量化的有损图象压缩算法。该算法首先利用小波变换对图象进行分解，然后根据各子带（子图象）特点对小波系数进行相应的改进 LBG向量量化编码，从而实现图象压缩目的。实验结果表明：该图象压缩算法的使用，可以在图象质量几乎不下降的条件下，极大地提高压缩比，改善恢复图象质量。