基于最小合成单元的维吾尔音库设计

为了实现容量小、可懂度和自然度较好的波形拼接式维吾尔语语音合成系统,结合维吾尔语的特点,完成了文本设计、录音、语音标注、以及音库建设过程。其中,把音节作为基本的合成单元建立了音节语音库;为了弥补音库中不存在音节的合成问题,又建立了音素作为合成单元的音素语音库。实验结果表明,以音节和音素为最小合成单元的波形拼接式维吾尔语语音合成系统除了音库容量相对少等特点之外,还具有良好的可懂度。     

游程编码与哈夫曼编码联合压缩实现技术

随着海量数据进入我们的生活,数据压缩成为计算机科学领域中十分活跃的论题,游程编码与哈夫曼编码是两种典型的无损压缩编码的方法,通过对两种编码压缩思想的分析,并对游程编码算法进行改进,将两者联合起来进行数据压缩,以期达到更大压缩比的目的。     

基于单帧干涉图的无损压缩的研究

受位消减法启发,针对单帧干涉图特点设计一种新的无损压缩方式,并分别采用哈夫曼编码、LZW编码以及算术编码作为新方法的熵编码部分对干涉图进行压缩,比较它们最终的压缩比。经Matlab仿真实验证明,三种编码方式最终取得的压缩比均高于位消减法,其中算术编码的压缩比最高,哈夫曼编码紧随其后,两者均能够取得较位消减法27%及以上的压缩比增长,LZW编码所取得的压缩效果相比前两者一般,不适合用于干涉数据的压缩。     

低速率语音编码技术与应用

介绍了语音压缩编码技术在模拟线路的数话混传以及在用户网关的实现两个方面的应用。产先通过分析当前铁路通信发展的要求与现状,提出了模拟线路数话混传的方案,并阐明了其优势所在。然后根据IP应用业务迅速增长的趋势提出了用户网关的概念及其基本实现,并将其推广到无线领域,提出无线用户网关的模型。最后给出结论：基于低速率语音编码的用户语音网关技术对实现模拟信道的数语混传、分组网络与其它网络的无缝连接起了关键的作用。     

应用PTIDR编码压缩的测试资源划分方法

提出了一种有效的新型测试数据压缩编码——PTIDR编码。该编码方法综合利用哈夫曼编码和前缀编码。理论分析和实验结果表明,在测试集中,0的概率p满足p≥0.7610时,能取得比FDR编码更高的压缩率,从而降低芯片测试成本。该编码方法的解码器也较FDR编码的解码器简单、易实现,且能有效节省硬件开销,并进一步节省芯片面积,从而降低芯片制造成本。     

对线性预测语音解压缩方法的探讨

本文针对常见的语音压缩方法,具体叙述了线性预测编码的基本原理及具体格式,并对算法实现的软件及硬件进行了详细的分析.     

中低速率语音编码技术的发展及应用

本文简要介绍了语音编码技术的产生与发展的历史，着重概述了当前中低速率语音编码技术的发展现状及各应用领域所采用的标准算法。     

SOC测试数据的编码压缩技术

文章介绍了一种基于测试向量集的压缩／解压缩方法，目的在于弥补SOC测试中,测试设备存储容量不足的问题，分析了三种不同的编码方案，并从压缩率和解码电路的规模对它们作了比较，得出了使用Golomb编码来进行测试向量压缩／解压缩是简单而又行之有效的方法的结论。文章还给出了一个有效的最小海明距离排序算法，大大的提高了测试数据的压缩率。     

一种低开销的压缩解压缩方法

针对LED大屏幕显示信息特点,深入分析RLE编码算法.改进游程长度编码模型,并结合Golomb码优势,提出了基于Golomb码的混合游程编码方法;其中分析了任意概率分布下Golomb码的结构,推导出任意给定阶数m和概率分布下的统一编码算法;最后给出了实现解码的硬件电路.通过对不同压缩方式比较,证明本文方法是一种低开销的、简便的、压缩效率高的方法.     

基于TTS模块的超声波障碍物检测语音播报系统设计

介绍一种基于超声波传感器和科大讯飞TTS语音模块的超声波障碍物距离检测及语音播报系统。系统首先利用单片机控制超声收发换能器探测目标障碍物信息,再经温度补偿后确定与目标障碍物距离,其后通过LCD及语音模块显示并播报测量结果。文中给出了系统的详细硬件设计方案及主要软件流程图。本系统结构简单,工作稳定,操作便利,反应速度快并具有智能语音播报功能,可用于盲人导航、机动车倒车或特定区域非法侵入检测等领域。     

Lossless compression of AVIRIS images

Adaptive DPCM methods using linear prediction are described for the lossless compression of hyperspectral (224-band) images recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The methods have two stages-predictive decorrelation (which produces residuals) and residual encoding. Good predictors are described, whose performance closely approaches limits imposed by sensor noise. It is imperative that these predictors make use of the high spectral correlations between bands. The residuals are encoded using variable-length coding (VLC) methods, and compression is improved by using eight codebooks whose design depends on the sensor's noise characteristics. Rice (1979) coding has also been evaluated; it loses 0.02-0.05 b/pixel compression compared with better VLC methods but is much simpler and faster. Results for compressing ten AVIRIS images are reported.     

Lossless compression of continuous-tone images

In this paper, we survey some of the recent advances in lossless compression of continuous-tone images. The modeling paradigms underlying the state-of-the-art algorithms, and the principles guiding their design, are discussed in a unified manner. The algorithms are described and experimentally compared     

Lossless compression of video sequences

We investigate lossless compression schemes for video sequences. A simple adaptive prediction scheme is presented that exploits temporal correlations or spectral correlations in addition to spatial correlations. It is seen that even with motion compensation, schemes that utilize only temporal correlations do not perform significantly better than schemes that utilize only spectral correlations. Hence, we look at hybrid schemes that make use of both spectral and temporal correlations. The hybrid schemes give significant improvement in performance over other techniques. Besides prediction schemes, we also look at some simple error modeling techniques that take into account prediction errors made in spectrally and/or temporally adjacent pixels in order to efficiently encode the prediction residual. Implementation results on standard test sequences indicate that significant improvements can be obtained by the proposed techniques     

Lossless compression of digital audio

Lossless audio compression is likely to play an important part in music distribution over the Internet, DVD audio, digital audio archiving, and mixing. The article is a survey and a classification of the current state-of-the-art lossless audio compression algorithms. This study finds that lossless audio coders have reached a limit in what can be achieved for lossless compression of audio. It also describes a new lossless audio coder called AudioPak, which low algorithmic complexity and performs well or even better than most of the lossless audio coders that have been described in the literature     

Lossless sliding-block compression of constrained systems

A method is presented for designing lossless sliding-block compression schemes that map constrained sequences onto unconstrained ones. The new compression scheme is incorporated into a coding technique for noisy constrained channels, which has applications to magnetic and optical storage. As suggested previously by Immink (see ibid., vol.43, p.1389-99, 1997), the use of a lossless compression code can improve the performance of a modified concatenation scheme where the positions of the error-correcting code and constrained code are reversed (primarily in order to eliminate error propagation due to the constrained code). Examples are presented that demonstrate the advantage of using sliding-block compression over block compression in a noisy constrained setting     

Lossless compression of multispectral image data

While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. The authors present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a prediction tree, which defines a noncausal prediction model for an image. The authors present a backward adaptive technique and a forward adaptive technique. They then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains is minimal     

Lossless compression of color mosaic images.

Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.     

Lossless compression of seismic signals using differentiation

For some classes of signals, particularly those dominated by low frequency components, such as seismic data first and higher order differences between adjacent signal samples are generally smaller compared with the signal samples. In this paper, evaluating the differencing approach for losslessly compressing several classes of seismic signals is given. Three different approaches employing derivatives are developed and applied. The performance of the techniques presented and the adaptive linear predictor are evaluated and compared for the lossless compression of different seismic signal classes. The proposed differentiator approach yields comparable residual energy compared with that obtained employing the linear predictor technique. The two main advantages of the differentiation method are: (1) the coefficients are fixed integers which do not have to be encoded; and (2) greatly reduced computational complexity, relative to the existing algorithms. These advantages are particularly attractive for real time processing. They have been confirmed experimentally by compressing different seismic signals. Sample results including the compression ratio, i.e., the ratio of the number of bits per sample without compression to those with compression using arithmetically encoded residues are also given     

干涉多光谱图像无损压缩算法

基于干涉多光谱图像成像原理和特点,提出一种干涉多光谱图像无损压缩算法。在压缩编码时,应充分利用图像的列相关性,采用基于列的比特平面编码和游程编码,对多光谱图像进行无损压缩,特别适于低分辨率的多光谱图像压缩。目前无损压缩算法的压缩比基本在1.6～2.4之间,本算法的压缩倍数一般可达到2倍以上,并且具有良好的抗误码性能。     

Lossless compression of video using temporal information

We consider the problem of lossless compression of video by taking into account temporal information. Video lossless compression is an interesting possibility in the line of production and contribution. We propose a compression technique which is based on motion compensation, optimal three-dimensional (3-D) linear prediction and context based Golomb-Rice (1966, 1979) entropy coding. The proposed technique is compared with 3-D extensions of the JPEG-LS standard for still image compression. A compression gain of about 0.8 bit/pel with respect to static JPEG-LS, applied on a frame-by-frame basis, is achievable at a reasonable computational complexity.