一种针对ISF参数的量化算法

宽带语音编码中普遍使用导抗谱频率描述声道。利用转换分类差矢量分裂矢量量化方法对导抗谱频率进行量化,该方法基于转换分类矢量量化及差值分裂矢量量化。IsF矢量先按照给出的码书分类,然后每一类中的差矢量再进行分裂矢量量化。实验结果表明,该算法可在每帧编码比特数为37时达到透明量化要求,并且码书存储量明显少于StephenSo等人给出的转换分类分裂矢量量化方法。     

语音LSF参数M-L搜索VQ算法研究

针对高效低速率语音编码,以LBG矢量量化码书设计算法为基础,研究了M-L搜索多级矢量量化(VQ)的码书设计算法和M-L搜索多级矢量量化编解码算法,同时对整个算法进行了全面的测试和性能分析。设计结果表明:该方法可有效提高LSF参数压缩的效率,改善谱失真指标。     

基于改进SMVQ的图像压缩算法

在图像编码方法中,矢量量化被认为是一种有效的低比特率图像编码方法.边匹配有限状态矢量量化利用相邻图像块之间的相关性避免了图像块边界之间大的灰度跃变.本文提出了一种改进的边匹配有限状态矢量量化,即双向低复杂度基于改进梯度的边匹配有限状态矢量量化.在双向低复杂度基于改进梯度的边匹配有限状态矢量量化中,第一次量化的状态码书尺寸由相邻图像块的梯度确定,第二次量化对第一次量化后的矢量中梯度值大于设定门限的矢量重新进行量化以提高图像质量.此外,和传统边匹配有限状态矢量量化利用上邻矢量和左邻矢量确定状态码书不同,新算法利用上、下、左、右四个相邻矢量来确定状态码书.试验结果表明,该算法的第二层编码在峰值信噪比上有1.5dB的改善;和传统的边匹配矢量量化相比较,在比特率相同时峰值信噪比平均有1.54dB的改善.     

基于空间组合矢量分类的小波图像网格编码矢量量化

本文基于零树编码、矢量分类和网格编码量化的思想,提出了对小波图像采用空间矢量组合和分类后进行网格编码矢量量化的新方法.该方法充分利用了各高频子带系数频率相关性和空间约束性,依据组合矢量能量和零树矢量综合判定进行分类,整幅图像只需单一量化码书,分类信息占用比特数少.对重要类矢量实行加权网格编码矢量量化,利用卷积编码扩展信号空间以增大量化信号间的欧氏距离,用维特比算法搜索最优量化序列,比使用矢量量化提高了0.6db左右.该方法编码计算复杂度适中,解码简单,可达到很好的压缩效果.     

一种快速模糊矢量量化图像编码算法

本文在学习矢量量化和模糊矢量量化算法的基础上,设计了一种新的训练矢量超球体收缩方案和码书学习公式,提出了一种快速模糊矢量量化算法。该算法具有对初始码书选取信赖性小,不会陷入局部最小和运算最小的优点。实验表明,ＦＦＶＱ设计的图像码书性能与ＦＶＡ算法相比,训练时间大大缩短,峰值信噪比也有改善。     

宽带ISF参数的非等系数帧间预测分裂矢量量化方法

 本文提出了一种新的适用于宽带语音编码ISF参数量化的非等系数帧间预测分裂矢量量化方案.该量化方案利用ISF参数的帧间相关性,基于预测分裂矢量量化原理,首先对待量化的ISF参数矢量进行去均值和非等系数帧间预测,然后对去均值后的ISF参数的预测残差进行分裂矢量量化.实验表明,该算法在每帧编码比特数为46bits时达到了透明量化,且平均谱失真比G.722.2中ISF参数量化的平均谱失真小.     

模糊C-均值(FCM)聚类法与矢量量化法相结合用于说话人识别

该文提出了一种将模糊C-均值聚类法与矢量量化法相结合进行说话人识别的方法。该算法将从语音信号中提取的 12阶 LPC(线性预测编码)倒谱系数作为待分类样本的 12个指标,先用矢量量化法求出每个说话人表征特征参数的码书,作为模糊聚类算法的聚类中心,最后将待识别的特征矢量以得到的码书为聚类中心,进行聚类识别。该算法所使用的特征参数较少,计算比较简单,但识别率较矢量量化法高。     

基于遗传算法的矢量量化

本文提出了一种基于遗传算法的矢量化方法。矢量量化码书设计本质是搜索训练矢量的最佳分类。遗传算法有卓越的全局优化搜索能力,易搜索到全局最优的矢量分类,形成高度优化的码书,可克服传统方法局部优化的缺陷。该算法不依赖初始条件、鲁棒性好、结构规则、并行性高。     

采用空间矢量组合的小波图像分类矢量量化

该文提出了采用空间矢量组合对小波图像进行分类矢量量化的新方法。该方法充分利用了各高频子带系数的频率相关性和空间约束性将子带系数重组，依据组合矢量能量和零树矢量综合判定进行分类，整幅图像只需单一量化码书，分类信息占用比特数少，并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化，提高了量化增益。仿真结果表明，该方法实现简单，在较低的编码率下，可达到很好的压缩效果。     

高效的模糊聚类初始码书生成算法

码书设计在矢量量化中至关重要,而多数码书设计算法都是基于初始码书的.从经典的LBG算法的缺陷出发,提出一种基于模糊聚类的高效初始码书生成算法,通过将初始码书的码矢在输入矢量空间中很好地散开,并尽可能占据输入概率密度较大的区域,从而使之后的LBG算法避免陷入局部最优,设计出的码书性能更好,更加接近全局最优,同时加快了收敛速度,减少了迭代次数.将该算法应用于图像编码的实验中,结果表明:该算法能够从效率和质量两方面有效地提高矢量量化的性能.     

一种基于高斯混合模型的导谱频率参数量化算法

提出了一种高效的基于高斯混合模型(GMM)的导谱频率(ISF)参数量化算法,算法的基本思想是利用高斯混合模型将导谱频率(ISF)参数发送给M个高斯簇,然后由高斯格型矢量量化器来量化相应高斯簇的导谱频率(ISF)参数,最终可以在M个量化值中选出频谱失真值最小的一个作为输出值。在设计高斯格型矢量量化器时,基于率失真理论提出了一种最佳比特分配算法。实验结果显示导谱频率(ISF)参数可以透明地压缩到42 bit/帧,与AMR-WB(G.722.2)的多级分裂矢量量化算法相比,节省了3 bit,减少了55%的存储空间。     

Encoding of Speech Spectral Parameters Using Adaptive Quantization Range Method

Efficient quantization methods of the line spectrum pairs (LSP) which have good performances, low complexity and memory are proposed. The adaptive quantization range method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector‐scalar hybrid quantizer. As the maximum quantization range of each LSP parameter is varied adaptively on the quantized value of the previous order's LSP parameter, efficient quantization methods can be obtained. The proposed scalar quantization algorithm needs 31 bits/frame, which is 3 bits less per frame than in the conventional scalar quantization method with interframe prediction to maintain the transparent quality of speech. The improved vector‐scalar quantizer achieves an average spectral distortion of 1 dB using 26 bits/frame. The performances of proposed quantization methods are also evaluated in the transmission errors.     

Transmit beamforming in multiple-antenna systems with finite rate feedback: a VQ-based approach

This paper investigates quantization methods for feeding back the channel information through a low-rate feedback channel in the context of multiple-input single-output (MISO) systems. We propose a new quantizer design criterion for capacity maximization and develop the corresponding iterative vector quantization (VQ) design algorithm. The criterion is based on maximizing the mean-squared weighted inner product (MSwIP) between the optimum and the quantized beamforming vector. The performance of systems with quantized beamforming is analyzed for the independent fading case. This requires finding the density of the squared inner product between the optimum and the quantized beamforming vector, which is obtained by considering a simple approximation of the quantization cell. The approximate density function is used to lower-bound the capacity loss due to quantization, the outage probability, and the bit error probability. The resulting expressions provide insight into the dependence of the performance of transmit beamforming MISO systems on the number of transmit antennas and feedback rate. Computer simulations support the analytical results and indicate that the lower bounds are quite tight.     

MMSE Multiuser Receiver for Uniformly Quantized Synchronous CDMA Signals in AWGN Channels

We analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels. The input-output relationship of the quantizer is represented by the gain-plus-additive-noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named "equivalent noise variance" which is a function of the sum of each active user's signal-to-noise ratio (SNR), processing gain, and the number of quantization levels. The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined. Simulation results validate the accuracy of our analysis.     

High-resolution quantization theory and the vector quantizeradvantage

The authors consider how much performance advantage a fixed-dimensional vector quantizer can gain over a scalar quantizer. They collect several results from high-resolution or asymptotic (in rate) quantization theory and use them to identify source and system characteristics that contribute to the vector quantizer advantage. One well-known advantage is due to improvement in the space-filling properties of polytopes as the dimension increases. Others depend on the source's memory and marginal density shape. The advantages are used to gain insight into product, transform, lattice, predictive, pyramid, and universal quantizers. Although numerical prediction consistently overestimated gains in low rate (1 bit/sample) experiments, the theoretical insights may be useful even at these rates     

Video coding scheme using DCT-pyramid vector quantization

A new and effective video coding scheme for contribution quality is proposed. The CMTT/2, a joint committee of CCIR and CCITT, has proposed a video coding scheme (already approved at European level by ETS) working at 34-45 Mbit/s. Basically this proposal includes a DCT transform for spatial correlation removal and motion compensation for temporal correlation removal. The individual transform coefficients are then scalar quantized with a non uniform bit assignment. Starting from the CMTT/2 proposal, the study presents a new video coding scheme designed using a vector quantizer solution instead of the scalar one. Specifically, the pyramid vector quantization (PVQ) has been chosen as the vector quantization method as it is able to reduce the DCT coefficients Laplacian distribution. Simulation results show that the proposed video coding scheme gives the same contribution quality at 22 Mbit/s as the one obtained with the CMTT/2 proposal at 45 Mbit/s.     

基于压缩感知的线谱对参数降维量化算法

为实现高质量的极低速语音编码,提出一种基于压缩感知理论的线谱对(LSP)参数降维量化算法。编码端利用压缩感知理论对超帧LSP高维矢量进行降维处理,将原始LSP参数投影到低维空间,得到低维测量值,然后采用分裂矢量量化算法对测量值进行量化;解码端以量化后的测量值为已知条件,利用正交匹配追踪算法重构出原始LSP高维矢量。实验结果表明,本算法相对低速语音编码中的矩阵量化方案,平均谱失真降低了0.23dB,相对基于DCT变换的降维量化方案,平均谱失真降低了0.13dB。这种先降维再量化的思想可以大幅减少编码所需的比特数及码本存储复杂度,有效降低语音编码速率,并且合成语音可懂度、自然度较高,音质虽有所失真,但基本上感觉不到明显的听觉质量下降。      

Variable rate vector quantization for speech, image, and videocompression

The performance of a vector quantizer can be improved by using a variable-rate code. Three variable-rate vector quantization systems are applied to speech, image, and video sources and compared to standard vector quantization and noiseless variable-rate coding approaches. The systems range from a simple and flexible tree-based vector quantizer to a high-performance, but complex, jointly optimized vector quantizer and noiseless code. The systems provide significant performance improvements for subband speech coding, predictive image coding, and motion-compensated video, but provide only marginal improvements for vector quantization of linear predictive coefficients in speech and direct vector quantization of images. Criteria are suggested for determining when variable-rate vector quantization may provide significant performance improvement over standard approaches