Embedded Image Coding on Wavelet Coefficients

The modeling of wavelet coefficient for still image compression is studied.Firstly,through simulation experiments on three classical wavelet coefficient models(i.e.,EZW model,SPIHT model/and EBCOT model),it is shown that SPIHT is superior to EZW in terms of compression performance(0.6 dB higher)and the compression performance of EBCOT is significantly better than that of SPIHT on average. Then the cause for such performance differences is explained through the analysis of models.Finally a new wavelet coefficients model is proposed which combines the advantages of the three classical models.Experimental results show that our model is better than the above mentioned three models with respect to compression ratio.     

Texture image retrieval using double density dual tree complex wavelet RH model

For using smooth probability density function to retrieve wavelet coefficient histogram and coefficient module histogram, parameter estimation is complicated, which results in hard to retrieve the texture features effectively. A texture image retrieval method using double density dual tree complex wavelet Refined Histogram(RH) model is proposed. By analyzing the principle of double density dual tree complex wavelet transform (DD-DT CWT) and the inherent relationship between the nonuniform quantizer and RH model, the RH model is extended to retrieve the DD-DT CWT coefficient and the coefficient histogram feature. The RH is used to model the magnitude of the DD-DT CWT. The RH parameters for all magnitude of complex coefficients forms the signature of an image. Image similarity measurement is accomplished by using the Kullback-Leibler divergences . The proposed method combines the advantages of the RH model and the shift-invariant DD-DT CWT. The experiment results show that the proposed methods yields higher retrieval rate than using the General Gaussian Density(GGD) model to fit with the real part or imaginary part of coefficients, and is better than using the Gamma PDF to fit with the magnitude of coefficients.     

A Context Model of Wavelet Coefficients for Image Compression

Through the combination of the idea of the linear prediction used in the GlicBawls coding scheme and the idea of the quantization taken in the CALIC coding scheme to reduce the number of contexts, a new context model of wavelet coefficients for image compression is proposed. Wavelet coefficients are encoded by the arithmetic encoder, with the contexts formed by quantizing linear prediction values. Experimental results show that the model achieves higher lossless compression rate of image than lossless SPIHT and lossless EBCOT used in JPEG2000. In addition, by exploiting the multiresolution property of wavelet, the model can compress the transformed image for progressive resolution and earn higher compression rate for each scale of the image than EBCOT.     

小波分析在大豆叶绿素含量高光谱反演中的应用

实测了不同水肥耦合作用下,大豆冠层高光谱反射率与叶绿素含量数据,并对光谱反射率、微分光谱与叶绿素含量进行了相关分析;采用叶绿素A与叶绿素B诊断波段构建了特定植被指数,对叶绿素A、叶绿素B进行了回归分析;采用小波分析对采集的光谱反射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对叶绿素含量进行估算。经分析发现,叶绿素A、B与光谱反射率在可见光与近红外波段的相关系数的变化趋势基本一致——在可见光谱波段呈负相关,近红外波段呈正相关,红边处相关系数由负变正。特定色素植被指数可以提高大豆叶绿素估算精度（R2>0.73）;小波能量系数回归模型可以进一步提高大豆叶绿素含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,叶绿素A其确定性系数R2为0.76,叶绿素B为0.78;以4变量与9变量回归分析结果表明：叶绿素A实测值与预测值的线性回归确定性系数R2分别大于0.85、0.89;叶绿素B实测值与预测值的线性回归确定性系数R2分别为0.86、0.90。     

VBR Coding Algorithm Based on Wavelet Transform

The introduction of the asynchronous transfer mode(ATM) concept has significantly influenced the coding theory.According to the characteristics of the ATM network,using the advantages based on the wavelet transform,how to apply the wavelet transform to plane separation and how to apply VBR coding to the layered data are discussed.The results of experiment are described respectively.     

Pulse Signal Analysis of Druggers with the Wavelet and Neural Network

The most significant difference between the human pulse signals collected from heroin druggers and healthy persons is at their amplitude waveforms as time functions. That is, the amplitude values and change rates of two types of signals, within a particular time range, appear different features. However, the partial components of the scaling and wavelet coefficients of the pulse signals obtained by using wavelet transform can reveal such key features. The pulse signals of 15 heroin druggers and 15 healthy persons are analyzed through using the muhiresolution analysis of wavelet transform. By using db2 orthogonal wavelet, every pulse signal is decomposed into three levels and the absolute values of the sixth component of scaling coefficients and the second component of the wavelet coefficients in the third level are combined to form a feature vector. A probabilistic neural network with good detection performance is successfully designed for automatically detecting 30 feature vectors. During the network design, 20 feature vectors are used as training samples. The remained 10 feature vectors are used as testing samples. Based on these steps, 15 heroin druggers and 15 healthy persons are all correctly identified. In other words, the detection rate arrives at 100%. druggers.     

A Medical Image Quantization Method Suited for PACS

Image compression is very important in picture archiving and communication system(PACS). The author studied the statistical distribution of image wavelet subimage coefficients and concluded that the distribution of wavelet subimage coefficients is similar to that of Laplasian distribution. On the other hand, in image reconstruction, the coefficient with different amplitude owns different weight, and different accuracy can be applied to different coefficients according to their different weight. Then, the author has designed a image quantization encoding scheme for PACS. In this scheme, they selected the sample-standard-deviation of coefficients in every subimage as the quantization threshold, and accurately encoded those coefficients with higher weight. Also, this algorithm utilized the visual character of human. The test has proved that the main advantages of this method are the simplicity in computing and predictable encoded coefficients, and a high compression efficiency can obtain too.     

Computation of the Wavelet and Scaling Functions Using Coefficients of Wavelet Filter

To associate the discrete wavelet transform with the continuous wavelet transform, an iterative convolution algorithm is given by analyzing the and scaling function using coefficients of wavelet filter. usually used methods of the computation The way of judging the convergence of improved algorithm on of the wavelet function iterative convolution is given. The advantages of improved algorithm is analyzed. The experimental result shows that the modified algorithm is effective.     

Video Compression of Vector Coding based on Wavelet Transform

It is possible to get high compression rate for the interrelationship between neighboring frames of moving pictures. The kernel of research on video compression is that the efficient image compression algorithm is adopted while the interrelationship between neighboring frames and time consumption is concerned to improve the compression efficiency. It is significant that motion compensation is applied to video compression, nevertheless it is too time consuming. A vector coding method based on wavelet transform is presented to avoid searching procedure of motion compensation. Simulation shows that high compression rate is achieved with less time consumption.     

FTIR结合小波变换分析鉴别8种根茎类作物

为了区分鉴别8种根茎类作物,通过采用傅里叶变换红外光谱(FTIR)结合小波变换(WI)、主成分分析(PCA)和聚类分析(HCA)的方法,测试研究了8种根茎类作物40个样品的红外光谱。结果表明：8种样品红外图谱相似,但在1800~700 cm-1范围内,红外光谱的峰位、峰形及吸收强度差异明显。对此范围内的原始红外光谱进行连续小波和离散小波变换。提取连续小波变换的第15层系数和离散小波变换的第5尺度细节系数数据,进行主成分分析和聚类分析。连续小波和离散小波的前3个主成分的累计贡献率分别为93.12%、89.78%,主成分分析和聚类分析正确率为100%。最终结果显示：傅里叶变换红外光谱技术结合小波变换的方法可以区分鉴别不同种的根茎类作物。     

Application of higher density wavelet transform to composite fault diagnosis of rolling bearing

Aiming at the difficulties in accurate reorganization of several weak faults currently, a composite fault diagnosis method based on higher density discrete wavelet transform and envelope spectrum is proposed. Firstly, the higher density discrete wavelet transform is used to decompose acquired vibration signals of rolling bearings. Then, the single-subband reconstruction is performed on the wavelet coefficients and scaling coefficients at each scale in order to solve frequency aliasing. Finally, the envelope spectra of all subband signals are calculated, and all faults can be recognized according to the characteristic frequencies of the typical faults. The proposed method is applied to the diagnosis of the rolling bearings with composite faults, and is compared with other common fault diagnosis method. The results show that the proposed method can be effectively used for the early composite fault diagnosis of rolling bearings.     

R-factor Pseudo-circular Extension Method

The extension methods of finite length signals for sub band coding associated with wavelet transformation are investigated.Considering the weakness of the existing extension methods,an extension method is presented which is named r factor pseudo circular extension method.It turns out theoretically that the input signal can be reconstructed exactly from the truncated section of the analyzed signals by our method and the method is better than other existing extension methods,as shown in many examples.A new valid way is provided for wavelet transform application in data compression.     

A Vector Quantification Image Coding Scheme Based on Wavelet Transform

The paper proposes an image compression and coding scheme based on discrete wavelet transform (DWT), which makes the best of the relativities among original image and coefficients of wavelet, the relativities among pixels of subimages, the relativities among directions of subimages. Human visual specialities were considered, so the compression scheme leads up to make full use of the statistical redundancy and visual redundancy of image. To get high compression ratio and good quality of image, the coefficients of image working on the quality of image greatly were reserved accurately, but the coefficients of image working on the quality of image slightly were quantified roughly. Also, the regrouping and coding of the coefficients could be used universally. The experimental results show that the compression method is provided with simply calculating, little time for coding and decoding and satisfying quality companied with high compression ratio.     

Optimal Design of Plane Cascades Based on Wavelet Theory

Based on the optimal control theory and Lagrange interpolation an optimal design model of plane cascades of 3D blade in an arbitrary surface of revolution is established. Using wavelet theory to solve the model, and lots of numerical results are obtained. The results show that the wavelet method is more effective than the conventional methods.     

N Periodic Multiresolution Analysis with Band M

N periodic multiresolution analysis with band M was introduced. By means of Fourier transformation and inner-product, N periodic orthogonal muhiresolution analysis with band M was studied. Expression of its scaling function space in frequency domain is obtained, the coefficients relationship of lowpass filter and highpass filter is investigated, and the elementary property of its mask is worked out. The results show that filter and mask of periodic wavelet is a series of coefficients.     

Rate control using the rate-distortion properties of the foreground and background regions

In order to improve the rate control performance of video encoder, video frames are segmented into two regions according to the video content, i.e. the foreground region containing the main moving objects and the background region containing the relatively static scene. The rate-distortion properties of the two regions are analyzed. Based on the analyses, a novel region-based macroblock layer rate control method for H.264/AVC is proposed. For the bit allocation process, more target bits can be assigned to the important macroblocks by employing an adaptive factor determined by the region information. Then, the quantization parameter (QP) is computed by using the quadratic model with different parameters related to the two regions. The computed QP is further adjusted to suppress the coding quality fluctuation between the two regions, as well as the quality fluctuation among the consecutive macroblocks. Finally, the rate control parameters are updated after coding the current macroblock. Experimental results demonstrate the mismatches between the actual coding bit-rates and the target bit-rates are reduced, while the coding performance of the encoder and the subjective visual quality of the reconstructed video are improved.     

Audio blind watermarking scheme combined DWT DCT QR

In order to protect the copyright of digital audio and video in Internet, we propose a novel audio blind watermarking scheme combined discrete wavelets transform, discrete cosine transform, QR decomposition and audio characteristics. In this algorithm, the audio are split into blocks, and each block are decomposed on two dimensional discrete wavelet transform (DWT), then the approximate sub band coefficients are decomposed on discrete cosine transform (DCT), and the first quarter of the DCT coefficients are decomposed on QR decomposition and get a triangle matrix. At last, the watermarking information is embedded into the triangle matrix. The experiments show that the algorithm can get better balance between transparency and robustness of watermark, and it has strong robustness against the common audio signal processing such as additive white Gaussian noise, re sampling, re quantization, low pass filter, MP3 compression and cutting replacement.     

Multi scale and multi orientation features for image de noising

An adaptive algorithm for image de noising is proposed based on the multi scale and multi orientation features. The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform. Then thresholds functions are adaptively set with these coefficients. The texture of the image information is introduced by using the mean of decomposition scale and the energy of regional. The greater the energy, the more information of the texture while the same decomposition scales, the smaller the threshold is set. On the contrary, the greater the threshold is set. After the de noising and then reconstruction of these coefficients, image de noising is implemented. Compare to the wavelet transform threshold and contourlet transform threshold, the nonsubsampled contourlet transform pick up the image detail better and improve the quality of the image.     

Multifocus image fusion scheme based on feature contrast in lifting stationary wavelet domain

A novel multifocus image fusion method based on lifting stationary wavelet transform (LSWT) is proposed. The selection principles, namely fusion rules of different subband coefficients, are discussed in detail. Local feature contrast is presented according to the human vision system (HVS), which is highly sensitive to the local image contrast level. Then, the fusion rule for the low-frequency subband coefficients fusion is introduced. To choose the high frequency subband coefficients, another local feature contrast is developed according to the human vision which is often sensitive to edges and directional features, but insensitive to real luminance at independent positions. Then, a novel fusion rule is proposed for fusion of the high frequency subband coefficients. Experimental results demonstrate that the proposed image fusion method is effective and can provide better performance in fusing multifocus images than the traditional contrast-based image fusion algorithms in term of informal visual inspection and objective criteria in multi-focus image fusion.