基于自适应增量调制的语音延时电路设计

传统的脉冲编码调制需要较长的码,其实现结构复杂。文中介绍了增量调制(DM)的原理,说明了它的优缺点,引入了对它改进后的调制方式——自适应增量调制(ADM)。自适应增量调制不仅集成了DM调制实现结构简单的特点,而且大大降低了DM调制中的量化噪声和斜率过载噪声,性能更为优良。根据ADM的原理,设计了采用自适应增量调制原理的语音延时电路,通过流片验证了其优异的性能,实测延时后的噪声电压为-88dBV,总谐波噪声+失真<0.5%。     

语音合成ASIC的原理与设计方法

本文阐述了语音合成专用集成电路(ASIC)的数字信号处理原理.采用波形自适应增量调制编码方法,研究并解决了语音信号的压缩与加密.用半定制标准单元CAD设计方法,CMOS工艺,完成了32K ROM 6秒语音ASIC芯片的设计.     

Requantization for transcoding of MPEG-2 intraframes

An investigation on requantization for transcoding of video signals is carried out. Specifically, MPEG-2 compatible discrete cosine transform (DCT) intraframe coding is addressed. The aim of this work is twofold: first, to provide a theoretical analysis of the transcoding problem, and second, to derive quantization methods for efficient transcoding based on the results of the analysis. The mean squared error (MSE) cost function is proposed for designing a quantizer with minimum distortion resulting in up to 1.3 dB gain compared with the quantizer used in the MPEG-2 reference coder TM5. However, the MSE quantizer leads in general to a larger bit rate and may therefore only be applied locally to blocks of sensitive image content. A better rate-distortion performance can be provided by the maximum a posteriori (MAP) cost function. In critical cases, the MAP quantizer gives a 0.4 dB larger signal-to-noise-ratio (SNR) at the same bit rate compared with the TM5 quantizer. The results are not limited to MPEG-2 and can be adapted to other coding schemes such as H.263 or JPEG.     

Rates of convergence in the source coding theorem, in empiricalquantizer design, and in universal lossy source coding

Rate of convergence results are established for vector quantization. Convergence rates are given for an increasing vector dimension and/or an increasing training set size. In particular, the following results are shown for memoryless real-valued sources with bounded support at transmission rate R. (1) If a vector quantizer with fixed dimension k is designed to minimize the empirical mean-square error (MSE) with respect to m training vectors, then its MSE for the true source converges in expectation and almost surely to the minimum possible MSE as O(√(log m/m)). (2) The MSE of an optimal k-dimensional vector quantizer for the true source converges, as the dimension grows, to the distortion-rate function D(R) as O(√(log k/k)). (3) There exists a fixed-rate universal lossy source coding scheme whose per-letter MSE on a real-valued source samples converges in expectation and almost surely to the distortion-rate function D(R) as O((√(loglog n/log n)). (4) Consider a training set of n real-valued source samples blocked into vectors of dimension k, and a k-dimension vector quantizer designed to minimize the empirical MSE with respect to the m=[n/k] training vectors. Then the per-letter MSE of this quantizer for the true source converges in expectation and almost surely to the distortion-rate function D(R) as O(√(log log n/log n))), if one chooses k=[(1/R)(1-ϵ)log n] for any ϵ∈(0.1)     

On incremental sigma-delta modulation with optimal filtering

The paper presents a quantization-theoretic framework for studying incremental sigma-delta (/spl Sigma//spl Delta/) data conversion systems. The framework makes it possible to efficiently compute the quantization intervals and hence the transfer function of the quantizer, and to determine the mean square error (MSE) and maximum error for the optimal and conventional linear filters for first and second order incremental /spl Sigma//spl Delta/ modulators. The results show that the optimal filter can significantly outperform conventional linear filters in terms of both MSE and maximum error. The performance of conventional /spl Sigma//spl Delta/ data converters is then compared to that of incremental /spl Sigma//spl Delta/ with optimal filtering for bandlimited signals. It is shown that incremental /spl Sigma//spl Delta/ can outperform the conventional approach in terms of signal-to-noise-and-distortion ratio. The framework is also used to provide a simpler and more intuitive derivation of the Zoomer algorithm.     

On the Delta Modulation of a First-Order Gauss-Markov Signal

Consider the delta-modulation (DM) of a first-order Gauss-Markov signalX. Let the adjacent-sample correlation inXbec, and let the (first-order) DM predictor coefficient bea. We express the quantizer input Qrin the form[S_{r} - aE_{r - 1} + (c - a)X_{r - 1}], where Sris an "innovations" term,aE_{r - 1}denotes the effect of quantizationerror(E)feedback and(c - a)X_{r - 1}reflects the effect of using ana neq c. For the important case ofc rightarrow 1(which models over-sampled DM inputs), we propose the simplifying assumption [7] of uncorrelatedXandE; with this assumption, our formalization of quantizer input leads very simply to interesting results in linear (LDM) and adaptive delta modulation (ADM). The LDM results are generalizations of known expressions for optimum values ofa, and the step-size Δ, and the value of signal-to-noise ratio SNR. For ADM, we derive optimum multiplier values for step-size adaptations with a one-bit memory, using the case ofc sim a = 1for simplicity. Our results depend on modeling instantaneous step-size adaptation as a mechanism for tracking the expected magnitude ofQ; existing literature has formalized such adaptation models only for the case of multi-bit quantizers.     

Adaptive quantization using piecewise companding and scaling for Gaussian mixture

Quantization is fundamental to analog-to-digital converter (ADC) and signal compression. In this paper, we propose an adaptive quantizer with piecewise companding and scaling for signals of Gaussian mixture model (GMM). Our adaptive quantizer operates under three modes, each of which corresponds to different types of GMM. Moreover, we propose a reconfigurable architecture to implement our adaptive quantizer in an ADC. We also use it to quantize images and design the tone mapping algorithm for high dynamic range (HDR) image compression. Our experimental results show that (1) the proposed quantizer is able to achieve performance close to the optimal quantizer (i.e., Lloyd–Max quantizer for GMM) in the sense of mean squared error (MSE), at much lower computational cost than it; (2) the proposed quantizer is able to achieve much better MSE performance than a uniform quantizer, at a cost similar to the uniform quantizer. The proposed adaptive quantizer holds great potential in the appilcations of the existing ADC and HDR image compression.     

Adaptive Companding of Picture Signals in a Predictive Coder

This paper describes a technique for varying the quantization characteristics in a Differential Pulse Code Modulation (DPCM) System. A multiplier, based on already transmitted picture elements (pels) which are spatially close to the pel being coded, is used to multiply the prediction error in a DPCM system. This multiplied prediction error is quantized by a fixed quantizer. Coarseness of quantization is thus dependent on the value of the multiplier, which is adjusted on the basis of the sensitivity of the human observers to quantization errors. The implementation is particularly useful in those cases where the quantizer outputs are coded using fixed length code words. Computer simulations on a variety of pictures indicate that a good quality picture can be produced by using a quantizer having between 8 to 10 levels and a two-dimensional predictor. Similar picture quality can be obtained by a simple nonadaptive previous element DPCM system having a quantizer with 13 to 16 levels.     

ROTATED BARNES-WALL LATTICE BASED VECTOR QUANTIZER AND ITS APPLICATION IN IMAGE-SEQUENCE CODING

A new scheme is presented to design a rotated Barnes-Wall lattice based vector quantizer(LVQ). The construction method of the LVQ and its fast quantizing algorithm are described at first. Then gain-shape lattice vector quantizer(GSLVQ) with LVQ as shape quantizer is discussed. Finally the GSLVQ is used in image-sequence coding and good experimental results are obtained.     

Vector Quantizer Design for Memoryless Gaussian, Gamma, and Laplacian Sources

Locally optimum vector quantizer (VQ) designs are presented for memoryless Gaussian, gamma, and Laplacian sources. For Gaussian sources, low (2-6) dimensional vector quantization provides relatively little improvement in mean-squared error (MSE) compared to the minimum mean-squared error (MMSE) scalar quantizer. For Laplacian or gamma sources, however, significant improvement in MSE is available with vector quantization. The Laplacian and gamma 6 bit, sixdimensional vector quantizers achieve, respectively, improvements of 2 and 4.5 dB over the corresponding scalar MMSE quantizer distortions.     

多光谱扫描仪（MSS）卫星遥感图象原始数据的信息保持型压缩编码

电视图象(隔行扫描)编码中采用的预测器：x^0=1/2x1′ 1/4(x3′ x4′)，同样适用于多光谱扫描仪(MSS)卫星遥感图象原始数据(逐行扫描)的信息保持型压缩编码．采用此预测器和简单的熵编码(A3码)，在两类计算机上用软件实现了典型MSS数据的信息保持型的编码／解码，压缩比略大于2．本文是课题报告及后续工作的简要报导，所提出的算法现JPEG建议中有关部分相似．     

基于旋转Barnes-Wall格的增益-波形矢量量化器及其在序列图象编码中的应用

本文首先提出了一种基于旋转Barnes-Wall格的格型矢量量化器(LVQ)的构造方法及快速量化算法,然后研究了以此LVQ为核心的增益-波形矢量量化器(GSLVQ)的实现方法,最后探讨了GSLVQ在序列图象编码中的应用方案,并给出了较好的实验结果。     

CS8831CN测试方法

CS8 8 31CN是用于语音录放的单片CMOSLSI,采用ADM (自适应增量调制 )。它与动态RAM以及包括话筒、扬声器、放大器等的音频电路共同构成一个完整的语音录放系统。     

加噪信源的高速WZ量化理论及其研究进展

研究了如何通过解码端含有边信息时获得未知信源的数据,即加噪信源的量化、编码及函数重建.将WZ高速量化及变换编码理论扩展到编码端对噪声进行观测的情况,并通过实验说明不同约束条件下的性能特点.验证了在构建最佳量化器的过程中能保持近似相同的率失真性能.     

Quantizers with uniform decoders and channel-optimized encoders

Scalar quantizers with uniform decoders and channel-optimized encoders are studied for a uniform source on [0,1] and binary symmetric channels. Two families of affine index assignments are considered: the complemented natural code (CNC), introduced here, and the natural binary code (NBC). It is shown that the NBC never induces empty cells in the quantizer encoder, whereas the CNC can. Nevertheless, we show that the asymptotic distributions of quantizer encoder cells for the NBC and the CNC are equal and are uniform over a proper subset of the source's support region. Empty cells act as a form of implicit channel coding. An effective channel code rate associated with a quantizer designed for a noisy channel is defined and computed for the codes studied. By explicitly showing that the mean-squared error (MSE) of the CNC can be strictly smaller than that of the NBC, we also demonstrate that the NBC is suboptimal for a large range of transmission rates and bit error probabilities. This contrasts with the known optimality of the NBC when either both the encoder and decoder are not channel optimized, or when only the decoder is channel optimized.     

基于视觉误差准则的矢量量化编码

传统矢量量化编码一般采用绝对误差为误差度量准则，并不符合人类视知觉的特性，本文根据人的视觉感知规律，定义了一种可广泛用于图象编码的视觉误差准则，由此导出了一种新的矢量量化编码方法，实验表明，采用这种方法得到的解码图象在视觉效果比传统方法有较大改善。     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).     

Adaptive block tree structure for video coding

The Joint Video Exploration Team (JVET) has studied future video coding (FVC) technologies with a potential compression capacity that significantly exceeds that of the high-efficiency video coding (HEVC) standard. The joint exploration test model (JEM), a common platform for the exploration of FVC technologies in the JVET, employs quadtree plus binary tree block partitioning, which enhances the flexibility of coding unit partitioning. Despite significant improvement in coding efficiency for chrominance achieved by separating luminance and chrominance tree structures in I slices, this approach has intrinsic drawbacks that result in the redundancy of block partitioning data. In this paper, an adaptive tree structure correlating luminance and chrominance of single and dual trees is presented. Our proposed method resulted in an average reduction of −0.24% in the Y Bjontegaard Delta rate relative to the intracoding of JEM 6.0 common test conditions.     

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     

MPEG－2视频编码的自适应量化器设计

本文在研究ＭＰＥＧ－２ＴＭ５建议的自适应量化策略的基础上，设计了一种新的自适应量化器。以块为基础分析宏块的局部视觉活动特性，并通过综合评价宏块中各块的视觉活动特性来最终决定自适应视觉量化因子。实验结果表明，本文所设计的自适应量化器能均匀分布图像编码主观失真，改善了图像质量，特别是减少了平坦区的块效应，降低了平坦区强边缘的失真。