采用空间矢量组合的小波图像分类矢量量化

该文提出了采用空间矢量组合对小波图像进行分类矢量量化的新方法。该方法充分利用了各高频子带系数的频率相关性和空间约束性将子带系数重组，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，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，在较低的编码率下，可达到很好的压缩效果。     

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.     

A fast quadtree motion segmentation for image sequence coding

A new fast quadtree-based variable size block matching scheme is devised for image sequence coding. The novelty of the method lies in the quadtree-based block matching with significantly less computations. In the proposed method, the computational requirements for matching are largely transferred from the evaluation of the mean absolute difference to the computations of the sum norms of the blocks which can be easily obtained. The fast quadtree decomposition scheme partitions an image frame into uniform motion blocks (larger blocks) and non-uniform motion blocks (small blocks). Standard VQ-based interframe coding is applied to uniform motion blocks while non-uniform blocks are intraframe coded. Implementation of this method shows a significantly lower computational requirement and a good-quality reconstruction of images at low bit-rates.     

Wavelet transform coding for MCP error images using quadtree decomposition and ECVQ

This paper proposes a new wavelet transform video coder which employs motion compensation, wavelet decomposition, and entropy-constrained vector quantization (ECVQ), in sequence. Each of layered subimages obtained from wavelet decomposition is segmented into basic blocks, and then the blocks are selectively encoded by ECVQ according to the energy of the samples. We introduce an efficient method to encode the map representing which blocks are encoded, based on inter-band prediction followed by a quadtree encoding. The proposed coder uses a simple forward analyzer in order to optimize the encoding parameters and introduces a preprocessing of signals which normalizes the input vectors of ECVQ in order to reduce the image-dependency of ECVQ codebooks. Simulation results show that our video coder provides good PSNR (peak-to-peak signal-to-noise ratio) performance and efficient rate control.     

图像多小波域矢量量化策略

提出一种基于多小波变换结合矢量量化的图像编码算法(MDWT VQ)。首先对图像进行多小波分解,然后对高频系数用改进后的LBG算法形成的码书进行VQ编码。算法充分利用了多小波域不同分辨率层间各方向子图像的相似性,仅对最高分辨率层进行码书地址索引,低级分辨率层的系数按照一定的组织形式直接套用最高分辨率层的地址索引信息。对比实验的结果验证了该算法在提高图像的重建质量以及在降低位码率方面均比传统的单小波图像编码算法有一定的提高。     

Hierarchical multirate vector quantization for image coding

The hierarchical multirate vector quantization (HMVQ) introduced in this paper is an improved form of vector quantization for digital image coding. The HMVQ uses block segmentation and a structure tree to divide an original image into several layers and sub-layers according to their grey scale contrast within blocks of a certain size. Variant bit-rates are used for block coding of different layers with the same codebook. The HMVQ technique provides high encoded image quality with very low bit-rates. The processing time for codebook generation is considerably reduced by using layer by layer optimization and subsampling in low detail regions. This technique also demonstrates flexibility of accurate reproduction in different detail regions.     

Multiscale image segmentation using fractal and neural network

Clustering algorithms in feature space are important methods in image segmentation. The choice of the effective feature parameters and the construction of the clustering method are key problems encountered with clustering algorithms. In this paper, the multifractal dimensions are chosen as the segmentation feature parameters which are extracted from original image and wavelet-transformed image. SOM (Self-Organizing Map) network is applied to cluster the segmentation feature parameters. The experiment shows that the performance of the presented algorithm is very good.     

采用方向树结构矢量组合的小波图像分类矢量量化

该文提出了采用方向树结构矢量组合对小波图像进行分类矢量量化的新方法。该方法的矢量构成结合了子带系数的方向性,充分利用了子带系数的带间和带内的相关性,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,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,可以达到很好的压缩效果。     

Predictive subband image coding with wavelet transform

Wavelet transform can decompose images into various multiresolution subbands. In these subbands the correlation exists. A novel technique for image coding by taking advantage of the correlation is addressed. It is based on predictive edge detection from the LL band of the lowest resolution level to predict the edge in the LH, HL and HH bands in the higher resolution level. If the coefficient is predicted as an edge it is preserved; otherwise, it is discarded. In the decoder, the location of the preserved coefficients can also be found as in the encoder. Therefore, no overhead is needed. Instead of complex vector quantization, which is commonly used in subband image coding for high compression ratio, simple scalar quantization is used to code the remaining coefficients and achieves very good results.     

Wavelet packet image coding using space-frequency quantization

We extend our previous work on space-frequency quantization (SFQ) for image coding from wavelet transforms to the more general wavelet packet transforms. The resulting wavelet packet coder offers a universal transform coding framework within the constraints of filterbank structures by allowing joint transform and quantizer design without assuming a priori statistics of the input image. In other words, the new coder adaptively chooses the representation to suit the image and the quantization to suit the representation. Experimental results show that, for some image classes, our new coder gives excellent coding performance.     

利用小波变换和约束矩阵进行图像压缩编码

本文将小波变换和矢量量化相结合对图像进行压缩编码,分析了图像经小波变换后的数据结构特性以及各小波分量之间的相关性。这种相关性的存在是由于小波变换具有良好的空－频域局部化特性。本文构造了结构约束矩阵来描述这种相关性,并以此为基础对传统的矢量量化算法进行了改进,改进后的算法减小了计算量,降低了码率。     

电子封装常用名称及术语汇集

电子封装常用名称及术语汇集下面，按英文字母顺序，汇集并解释了与目前ＬＳＩ（包括ＩＣ）正在采用的主要封装形式相关联的名称术语等。这些名称术语参考并引用了日本国内１２个半导体制造公司，其他国家７个半导体制造公司＊与ＬＳＩ封装相关的资料、日本电子机械工业会...     

结合矢量量化的SPIHT算法用于多光谱图像压缩

针对多波段遥感图像纹理复杂丰富、局部相关性较弱的特点,提出了结合矢量量化的SPIHT压缩算法。将经过小波变换后的遥感图像谱间相同位置的系数聚集构成矢量,根据高频子图的局部块纹理强弱进行自适应性的量化。使基于标量的SPIHT算法能够方便的处理矢量,有效去除数据间各类相关。实验表明,该方法对多波段遥感图像的压缩可以收到良好的效果,且算法具有良好的实时性,对单幅图像的压缩比和峰值信噪比(PSNR)均优于普通的二维SPIHT算法。     

Watermarking based image authentication and tamper detection algorithm using vector quantization approach

In the present work, a novel image watermarking algorithm using vector quantization (VQ) approach is presented for digital image authentication. Watermarks are embedded in two successive stages for image integrity verification and authentication. In the first stage, a key based approach is used to embed robust zero level watermark using properties of indices of vector quantized image. In the second stage, semifragile watermark is embedded by using modified index key based (MIKB) method. Random keys are used to improve the integrity and security of the designed system. Further, to classify an attack quantitatively as acceptable or as a malicious attack, pixel neighbourhood clustering approach is introduced. Proposed approach is evaluated on 250 standard test images using performance measures such as peak signal to noise ratio (PSNR) and normalized hamming similarity (NHS). The experimental results shows that propose approach achieve average false positive rate 0.00024 and the average false negative rate 0.0012. Further, the average PSNR and tamper detection/localization accuracy of watermarked image is 42 dB and 99.8% respectively; while tamper localization sensitivity is very high. The proposed model is found to be robust to common content preserving attacks while fragile to content altering attacks.     

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).     

Distributed video coding based on vector quantization: Application to capsule endoscopy

We present in this paper a new distributed video coding (DVC) architecture for wireless capsule endoscopy. It is based on the state of the art DVC systems, but without using key frames. Instead, it uses an adapted vector quantization (VQ) with a searching complexity that is shifted to the decoder. VQ allows creating a good side information (SI) by exploiting the similarities in human anatomy. Thus, SI is created from a codebook (CB) rather than by motion compensated prediction. This approach decreases largely the complexity of the encoder, which codes only Wyner-Ziv frames, and allows a progressive decoding. The encoder of the proposed DVC generates only a simple hash that is used by the decoder to select the corresponding VQ codeword. The obtained experimental results show that rate-distortion results are better than those of JPEG, and show the possibility of using scalable coding to control the used rate and energy.     

小波变换在图象压缩中的应用

文章从实际应用角度出发,提出了一种基于小流变换和改进LBG向量量化的有损图象压缩算法。该算法首先利用小波变换对图象进行分解,然后根据各子带（子图象）特点对小波系数进行相应的改进LBG向量量化编码,从而实现图象压缩目的。实验结果表明：该图象压缩算法的使用,可以在图象质量几乎不下降的条件下,极大地提高压缩比,改善恢复图象质量。     

Radiographic image sequence coding using two-stage adaptive vector quantization

A two-stage adaptive vector quantization scheme for radiographic image sequence coding is introduced. Each frame in the sequence is first decomposed into a set of vectors, corresponding to nonoverlapping spatially contiguous block of pixels. A codebook is generated using a training set of vectors drawn from the sequence. Each vector is then encoded by the label representing the closest codeword of the codebook, and the label values in a frame label map memory at both ends of the communication channel. The changes occurring in the radiographic image sequences can be categorized into two types: those due to body motion and those due to the injected contrast dye material. In the second scheme proposed, encoding is performed in two stages. In the first stage, the labels of corresponding vectors from consecutive frames are compared and the frame label map memory is replenished (updated). This stage is sufficient to tack the changes caused by patient motions but not due to the injected contrast dye material. The resulting residual error vectors after the first stage coding are calculated for the latter changes and are further encoded by a second codebook, which is updated on a frame-to-frame basis.     

一种基于小波变换的低比特率混合图像编码方法

本文提出了一种基于小波变换的低比特率图像编码方法,利用人的视觉特性对高频高活跃度系数进行调制,为了保护图像能量,对高频高能系数进行标量量化后采用自适应算术编码,采用高分辨率级子图像矢量分类信息由低分辨率级子图像矢量自动产生,提高了压缩比。实验表明采用此编码方法在高压缩比的情况下获得了较好的图像质量。     

Wavelet transform image coding using lattice vector quantisation

A novel method for the efficient coding of image wavelet coefficients using zerotree multistage lattice vector quantisation is presented. This method achieves high compression ratios with good picture quality, maintaining a very simple implementation. Simulation results demonstrate that the coding performance of this algorithm favourably compares to some of the best reported image compression results