This paper introduces an improved classification algorithm of wavelet coefficients in image coding using EZW.
本文提出了图像EZW编码中一种新的分类算法。
The wavelet coefficients are selected by Human Visual System (HVS) to ensure the transparence of watermarking.
同时基于人眼视觉系统，对嵌入系数进行筛选以保证水印的透明性。
The strategy of segment transform and the whole wavelet coefficients modification is adopted to improve the robustness of the digital watermark.
采纳分段变换及整体修改策略，增强了数字水印嵌入的稳定性；
ZBP exploits the correlation among the Zerotree symbols and the bit data of wavelet coefficients, so the efficiency of arithmetic coding is improved.
ZBP不仅充分利用了零树符号之间的相关性，而且从位数据的层面上挖掘出了小波系数值之间的相关性，从而提高了算术编码的性能。
Analyze the space correlation in the single differential image: sample the wavelet coefficients with window and segment the image into the equal blocks.
分析单幅差分图像的空间相关性：使用采样窗口对单幅差分图像的小波域采样并将图像分割成若干个均匀分布的小块。
Furthermore, by evaluating three parameters of wavelet coefficients "probability model using maximum likelihood method, self adaptively of the algorithm is achieved."
而且通过极大似然法估计小波系数概率模型中的三个参数，使算法达到自适应性。
Before quantifying, the wavelet coefficients should be given a vision weight, and then organized in the way of across different subbands according to the same direction.
进行矢量量化前，首先对小波系数进行视觉加权，然后采用了同向跨频带的方法将小波系数进行矢量组织。
Furthermore, through the maximum likelihood used for evaluating three parameters of wavelet coefficients' probability model, the adaptive algorithm is derived in the article.
而且本文通过极大似然法估计小波系数概率模型中的三个参数，使算法达到自适应。
Have analyzed properties of wavelet coefficients obtained from an image through wavelet transform, and discussed how to select wavelet basis to optimize wavelet coefficients.
分析了图像小波变换后小波系数的特征，讨论了优化小波系数的小波基选择问题。
A feature indexing algorithm based on wavelet coefficients is used when comparing features in neighboring images, which increases efficiency in the nearest neighbor searching.
在进行相邻图片的特征比对时，提出一种基于小波系数的特征索引算法，提高搜索效率。
The quality of image processing can be significantly improved by exploiting the correlation among image wavelet coefficients and modeling the statistics for these coefficients.
研究图像小波系数间的统计相关性并建立适当的模型，可以显著提高图像处理的质量。
It provides optimal dynamic bit allocation for sub-bands of DWT and realizes adaptive quantization for wavelet coefficients. At last, it provides exact rate control for the coder.
该方法基于率失真曲线的精确理论模型，对小波分解后的不同子带提供最优的动态比特分配从而实现了小波系数的自适应量化编码，最后还实现了码率控制。
Based on the primary qualitative method, we propose an annual runoff prediction model using weighted sum of wavelet coefficients of major periods to predict the periodic components.
本文在原有定性分析方法的基础上，提出基于年径流时间序列主周期小波系数加权求和预测周期成分的年径流预测模型。
The traditional pitch detection algorithm based on wavelet transform extracts the fundamental frequency by comparing the positions of maximum wavelet coefficients of continuous scales.
传统的小波变换基频检测通过比较相邻尺度上的小波系数极值点来进行检测。
Data were embedded into the Least Significant Bit-plane (LSB) of high frequency CDF (2, 2) integer wavelet coefficients, which accorded with the principle of Human Visual System (HVS).
在图像的小波子带系数的最低位(LSB)嵌入隐藏数据，将数据嵌入到小波cdf(2,2)的高频子带系数中，符合人的视觉系统原理(HVS)。
A regularized image restoration is the optimization for some conditional constraint, and the selection of wavelet coefficients based Bayesian statistic is on the image random field view.
正则化图像恢复是条件约束的最优化问题，而小波系数的贝叶斯统计选择是基于图像的随机场观点。
Based on wavelet decomposition and cepstrum technology, an audio watermarking algorithm was proposed in which the low-frequency wavelet coefficients were chosen for cepstrum transforming.
提出了一种基于小波分解和倒谱技术的音频数字水印算法，该算法通过对原始音频进行小波多级分解，从中选取低频系数进行倒谱变换。
The method of threshold in deleting noise has two kinds of ways. One of them is hard one and the other is soft. The main idea is based on the processing method of the wavelet coefficients.
小波阈值去噪分为硬阈值去噪和软阈值去噪，它们的去噪思想都是在小波分解后的各层系数中，对模大于和小于某个阈值的系数分别进行处理。
After the clustering characteristics of image wavelet coefficients' significance distribution in each sub-band was analyzed, run-length coding (RLC) was introduced into SPIHT's output stage.
针对图像小波系数在各子带内显著性分布的聚簇特征，提出了在SPIHT算法的输出环节引入游程编码。
Finally, the initial threshold is identified and relation matrix is constructed, and the wavelet coefficients of the high frequency subbands are encoded progressively by using relation matrix.
首先确定初始阈值，并构造小波系数的关系矩阵，然后结合关系矩阵对高频子带系数进行逐次逼近量化编码。
The algorithm, which multiplies the corresponding wavelet coefficients of adjacent fine scales, enhances the feature of discontinuity point of signals and inhibits the effect of noise signals.
该算法将相邻两个细尺度层上的小波变换系数对应相乘，从而增强了信号在突变点处的特征并抑制了噪声的影响。
Triplets of wavelet coefficients are defined, then selected and classified into two classes to embed and extract the watermark according to the relationship of their three wavelet coefficients.
定义了三重小波系数集，根据其元素间相互关系选择系数集并将其分成两类从而嵌入和提取水印。
Second, the wavelet coefficients are reconstruct based on the top few principle components and the local noise energy is estimated based on the mean energy of reconstructed wavelet coefficients;
然后以主分量对小波系数进行重建的平均能量作为局部噪声能量的估计；
We have derived the formulas for wavelet coefficients of the fractal signal and white noise separately on the basis of which the maximum likelihood method is used to estimate the fractal parameter.
分别推导了分形信号和白噪声的小波系数统计量的公式，在此基础上用最大似然法进行参数估计。
Finally, it gave an image de-noising algorithm of coherence enhancing diffusion, which used wavelet coefficients to estimate the image edge according to the wavelets time-frequent analysis function.
针对相干增强扩散计算扩散矩阵较慢的缺点，提出了一种用小波系数估计图像边缘方向的相干增强扩散图像降噪算法。
For the image high-frequency part, use the new evaluation factor - wavelet neighborhood information to choose the ultimate wavelet high-frequency coefficients.
使用由方差和平均梯度构造的新的评价因子——小波邻域信息量作为融合规则选取小波高频系数。
A method of calculating wavelet transformation filter coefficients for multiscale edge detection is given in this paper, and the efficiency of the method is clarified.
提出了一种用于多尺度边缘检测的小波变换滤波器系数的计算方法，并且以具体例子进行了检验，表明了该方法的有效性。
Based on expatiate wavelet transform principle, aimed at the construction of fusion operator, a new scheme using the correlation of decomposition coefficients is presented.
文中在阐述小波变换原理的基础上，针对融合算子的构造，给出了一种新的基于小波系数相关性的融合方法。
In this article, the linear magnitude predictor of wavelet packet decomposition coefficients, the creation algorithm of images characteristics based on wavelet packet transform have been introduced.
该文建立了自然图像的小波包分解系数绝对值线性预测模型，提出了基于小波包变换的图像特征形成算法。
In this article, the linear magnitude predictor of wavelet packet decomposition coefficients, the creation algorithm of images characteristics based on wavelet packet transform have been introduced.
该文建立了自然图像的小波包分解系数绝对值线性预测模型，提出了基于小波包变换的图像特征形成算法。
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