一种用于WI语音编码的相位预测式矢量量化方法

在传统的低比特率语音编码中,考虑到人耳对相位信息不敏感而经常忽略相位信息,这将导致语音粗糙、刺耳甚至音调发生改变。为了获得高质量的声码器,语音的相位信息是不能不考虑的。该文在散布相位矢量量化方法的基础上进一步去除了相位冗余,在波形内插(Waveform Interpolation,WI)编码模型中对相邻帧慢渐变波形(Slowly Evolving Waveform,SEW)的相位谱差值进行预测式矢量量化。实验发现,该方法大大改善了重建语音效果,明显提高了语音的自然度和清晰度。主观A/B测试结果显示,该方法与固定相位法相比,经4~6 bit的相位量化可使合成语音质量得到显著的改善,相比散布相位矢量量化方法,女声的语音合成质量有所改进。     

Design, performance, and complexity analysis of residualtrellis-coded vector quantizers

Multistage trellis-coded vector quantization (MS-TCVQ) is developed as a constrained trellis source-coding technique. The performance of the two-stage TCVQ is studied for Gaussian sources. Issues of stage-by-stage design, output alphabet selection, and complexity are addressed with emphasis on selecting and partitioning the stage codebooks. For a given rate, MS-TCVQ achieves low encoding and storage complexity compared to TCVQ, and comparisons with same-dimensional multistage vector quantization indicate a 0.5-3-dB improvement in signal-to-quantization-noise ratio     

Trellis coded vector quantization

A vector generalization of trellis coded quantization (TCQ), called trellis coded vector quantization (TCVQ), and experimental results showing its performance for an i.i.d. Gaussian source are presented. For a given rate, TCVQ yields a lower distortion that TCQ at the cost of an increase in implementation complexity. In addition, TCVQ allows fractional rates, which TCQ does not     

网格编码矢量量化在SAR原始数据压缩中的应用

本文提出了一种结合传统的块自适应量化与网格编码矢量量化对SAR原始数据进行压缩的方法。首先对SAR原始数据实施块自适应量化以减小数据的动态范围,然后对块自适应量化后的数据进行网格编码矢量量化。该方法在考虑了SAR原始数据特性的基础上较好地利用了矢量的空间相关性和信号的时间相关性,进一步提高了量化增益。仿真结果表明,该方法在SAR原始数据的压缩中取得了很好的效果。     

Jointly optimized trellis-coded residual vector quantization

The union of residual vector quantization (RVQ) and trellis-coded vector quantization (TCVQ) was considered by various authors where the emphasis was on the sequential design. We consider a new jointly optimized combination of RVQ and TCVQ with advantages in all categories. Necessary conditions for optimality of the jointly optimized trellis-coded residual vector quantizers (TCRVQ) are derived. A constrained direct sum tree structure is introduced that facilitates RVQ codebook partitioning. Simulation results for jointly optimized TCRVQ are presented for memoryless Gaussian, Laplacian, and uniform sources. The rate-distortion performance is shown to be better than RVQ and sequentially designed TCRVQ     

Motion adaptive video coding scheme for time‐varying network

Adaptive video coding algorithms can encode a video stream dynamically on the basis of the amount of bandwidth available in a network. In this paper, a novel approach for adaptive video coding based on look‐up tables has been proposed. On the basis of the network conditions, the proposed codec estimates quantization parameter (QP) and also the spatial resolution for a group of pictures from the look‐up table. Then, QP for each frame is estimated on the basis of the motion content in each frame. More motion leads to more burden of bits while encoding the motion vectors. Also, quantization noise is less prominent in high‐motioned frames than frames possessing low motion information because of the temporal masking phenomenon of the human eyes. So, the main trick of the proposed scheme is to assign higher QP to the frames having higher motion than those having less motion. This method also reduces the requirement of excess bits for encoding of motion vectors having higher motion content. Additionally, QP is adjusted on the basis of the buffer availability in order to avoid bit loss error. Experimental results show the efficacy of the proposed codec. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

一种基于MELP模型600bps声码器的设计

基于增强型混合激励线性预测(MELPe)模型,设计了一款600bps低速率语音编码器。该编码器在保持MELPe算法特征的同时,利用相邻帧的帧间冗余,把连续的三帧构成一个超帧,对超帧采用多模式预测和多级矩阵量化技术进行联合量化。同时针对超帧的不同模式,通过预测系数对相邻超帧的模式转换进行处理,实现线谱对参数(LSF)的矢量量化。最后对基音周期与增益参数进行联合量化,进一步提高量化效率,完成一款在600bps下仍具有较好合成语音质量的语音编码器的设计。     

基于方向树结构矢量分类的小波图像网格编码矢量量化

本文提出了采用方向树结构矢量组合并分类对小波图像进行网格编码矢量量化（ＴＣＶＱ）的新方法。该方法矢量构成结合了子带系数的方向性，充分利用了子带系数带间和带内相关性，按能量和活跃度进行两级分类，降低了类中矢量的内部离散度，对活跃和非活跃类矢量实行加权ＴＣＶＱ，利用卷积编码扩展信号空间，用维特比算法搜索最优量化序列，比使用加权 ＶＱ提高了 ０．７ｄｂ左右。该方法编码计算复杂度适中，解码简单，有较好的压缩效果。     

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。     

EAC音频编码技术

利用一种余弦调制多分辨率分析技术提供的高性能频率选择能力，设计了一套完整的音频编解码器。与其它先进的音频编码技术（如MPEG AAC、MP3和AC－3等）相比，该编码技术的多分辨率滤波技术、信号类型判断技术、心理学计算技术和量化技术有其鲜明的特点和优势。实验表明，在没有实现完全自适应的情况下，其单声道编码效率高于优化的AAC编码器核心编码单元的编码效率。     

One-Step interpolation Predictive Vector Quantization of LSP Parameters

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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.     

一种基于小波变换和矢量量化的图像压缩算法

小波变换和矢量量化都是图像压缩中的重要方法。利用小波变换的系数特点,对图像进行小渡分解,对于能量最为集中的低频分量采用标量量化处理,然后将标量量化过程中产生的残差和高频分量一起构造矢量,进行矢量量化。实验结果表明,此算法能够有效提高重构图像质量,获得较高的信噪比。     

量化方法及其统计特征量用于图像检索的性能比较

分别对标量量化,矢量量化以及分类矢量量化等不同量化方法及其统计特征量用于图像检索的性能进行了分析和比较,对进一步实现支持检索的图像压缩算法具有一定的指导意义。     

一种指数型模糊学习矢量量化图像编码算法

本文分析了模糊矢量量化（ＦＶＱ）图像编码的原理，提出了一种指数型模糊学习矢量量化算法（ＥＦＬＶＱ）。实验结果表明，该算法具有快速收敛性能，设计的图像码书峰值信噪比与ＦＶＱ算法相比也略有改善。     

DCT域网格编码矢量量化

本文提出了在离散余弦变换(DCT)域作网格编码矢量量化(TCVQ),并利用量化后相关性的新方法.该方法结合了DCT变换、矢量量化和网格编码量化的优点,它充分利用信号DCT域和时域的相关性来逼近率失真下界,并利用维特比算法来寻找最佳量化序列.同时,它还利用量化后码字间存在的相关性来进一步降低编码率.仿真结果表明,该方法在保持高压缩比的同时具有很高的信噪比性能.在相同编码率下它比全搜索矢量量化(VQ)好1dB左右.同时该方法具有计算量适中,解码简单以及对误差扩散不敏感的优点.     

Noncausal predictive image codec

The paper describes a lossy image codec that uses a noncausal (or bilateral) prediction model coupled with vector quantization. The noncausal prediction model is an alternative to the causal (or unilateral) model that is commonly used in differential pulse code modulation (DPCM) and other codecs with a predictive component. We show how to obtain a recursive implementation of the noncausal image model without compromising its optimality and how to apply this in coding in much the same way as a causal predictor. We report experimental compression results that demonstrate the superiority of using a noncausal model based predictor over using traditional causal predictors. The codec is shown to produce high-quality compressed images at low bit rates such as 0.375 b/pixel. This quality is contrasted with the degraded images that are produced at the same bit rates by codecs using causal predictors or standard discrete cosine transform/Joint Photographic Experts Group-based (DCT/JPEG-based) algorithms.     

An Efficient Information Hiding Scheme Based on Closest Paired Tree Structure Vector Quantization

Information hiding schemes based on vector quantization (VQ) usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization (CPTSVQ). The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.     

采用树结构矢量组合的小波图像分类矢量量化

本文提出了采用树结构矢量组合对小波图像进行分类矢量量化的新方法.该方法充分利用了子带系数的带间和带内的相关性,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,可达到很好的压缩效果.