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。     

Progressive image transmission using vector quantization on imagesin pyramid form

A progressive image transmission scheme in which vector quantization is applied to images represented by pyramids is proposed. A mean pyramid representation of an image is first built up by forming a sequence of reduced-size images by averaging over blocks of 2×2 pixels. A difference pyramid is then built up by taking the differences between successive levels in the mean pyramid. Progressive transmission is achieved by sending all the nodes in the difference pyramid starting from the top level and ending at the bottom level. The kth approximate image can be formed by adding the information of level k to the previously reproduced (k-1)st approximation. To gain efficiency, vector quantization is applied to the difference pyramid of the image on a level-by-level basis. If the errors due to quantization at level k are properly delivered and included in the next level, k+1, then it is demonstrated that the original image can be reconstructed. An entropy coder is used to encode the final residual error image losslessly, thus ensuring perfect reproduction of the original image. The experiments demonstrate that it is possible to achieve simultaneously lossless and progressive transmission with compression. At the intermediate level, the use of vector quantization results in a coding gain over that obtained using only a Huffman coder. Excellent reproduction is achieved at a bit rate of only 0.06 bits/pixel     

Scan predictive vector quantization of multispectral images

Conventional vector quantization (VQ)-based techniques partition an image into nonoverlapping blocks that are then raster scanned and quantized. Image blocks that contain an edge result in high-frequency vectors. The coarse representation of such vectors leads to visually annoying degradations in the reconstructed image. The authors present a solution to the edge-degradation problem based on some earlier work on scan models. The approach reduces the number of vectors with abrupt intensity variations by using an appropriate scan to partition an image into vectors. They show how their techniques can be used to enhance the performance of VQ of multispectral data sets. Comparisons with standard techniques are presented and shown to give substantial improvements.     

Vector-adaptive vector quantization with application to speechcoding

A vector-adaptive vector quantization (VAVQ) scheme, which may be viewed as a generalization of gain-adaptive vector quantization, is described. The proposed scheme adjusts each component of the encoding signal vector according to a statistical estimate of the signal characteristics. The VAVQ scheme can cope with large input dynamic ranges. It can be used in either the time domain or the transform domain, and exhibits approximately 4 dB improvement in segmental signal-to-noise ratio (SNR) over the fixed VQ     

Interpolative BTC image coding with vector quantization

The authors suggest two interpolative block truncation coding (BTC) image coding schemes with vector quantization and median filters as the interpolator. The first scheme is based on quincunx subsampling and the second one on every-other-row-and-every-other-column subsampling. It is shown that the schemes yield a significant reduction in bit rate at only a small performance degradation and, in general, better channel error resisting capabilities, as compared to the absolute moment BTC. The methods are further demonstrated to outperform the corresponding BTC schemes with pure vector quantization at the same bit rate and require minimal computations for the interpolation     

Previsualized image vector quantization with optimized pre- andpostprocessors

The optimal previsualized image vector quantization method for compressing digital images to a bit rate of 0.75 bpp or below with moderately low to very low subjective distortion is presented. The encoding method incorporates a visual model as part of the distortion measure. By modeling the quantization noise as an additive signal-dependent noise process, an optimum pre- and postprocessing system, which minimizes the mean-squared error measured inside the visual model, is derived. The analysis of the system performance and a coordinate descent design algorithm are discussed. A set of experiments was conducted using the optimum system, and the results were compared to those obtained by other methods. The study shows that the images quantized by the method presented exhibit much less sawtooth, blocking, and contouring effects and higher subjective quality. Images of surprising quality have been produced by this method at a bit rate of about 0.1 bpp with a compression ratio of 80:1 relative to a normal 8 bpp original     

Progressive image coding using trellis coded quantization

In this work, we present coding techniques that enable progressive transmission when trellis coded quantization (TCQ) is applied to wavelet coefficients. A method for approximately inverting TCQ in the absence of least significant bits is developed. Results are presented using different rate allocation strategies and different entropy coders. The proposed wavelet-TCQ coder yields excellent coding efficiency while supporting progressive modes analogous to those available in JPEG.     

二维网格编码矢量量化及其在静止图像量化中的应用

该文提出了在二维码书空间中，在矢量量化（VQ）的基础上，应用网格编码量化（TCQ）的思想来实现量化的新方法－－二维网格编码矢量量化（2D－TCVQ）。该方法首先把小码书扩展成大的虚码书，然后用网格编码矢量量化（TCVQ）的方法在扩大的二维码书空间中用维物比算法来寻找最佳量化路径。码书扩大造成第一子集最小失真减小从提高了量化性能。由于二维TCVQ采用的码书尺寸较小，因而可以应用到低存贮、低功耗的编解码环境。仿真结果表明，同一码书尺寸下，二维TCVQ比TCVQ好0．5dB左右。同时，该方法具有计算量适中，解码简单以及对误差扩散不敏感的优点。     

Using vector quantization for image processing

A review is presented of vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, which is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the compression step. Recent work has used vector quantization both to simplify image processing tasks, such as enhancement classification, halftoning, and edge detection, and to reduce the computational complexity by performing the tasks simultaneously with the compression. The fundamental ideas of vector quantization are explained, and vector quantization algorithms that perform image processing are surveyed     

Progressive image transmission by transform coefficient residualerror quantization

A progressive image transmission scheme based on iterative transform coding structure is proposed for application in interactive image communication over low-bandwidth channels. The scheme not only provides progressive transmission, but also guarantees lossless reproduction combined with a degree of compression. The image to be transmitted undergoes an orthogonal transform, and the transform coefficients are quantized (scalar or vector) before transmission. The novelty is that the residual error array due to quantization is iteratively fedback and requantized (scalar or vector); the coded residual error information is progressively transmitted and utilized in reconstructing the successive approximations. It is shown that the average reconstruction error variance converges to zero as the number of iterative stages approaches infinity. In practice, lossless reproduction can be achieved with a small number of iterations by using an entropy coder on the final residual-error image. Computer simulation results demonstrate the effectiveness of the technique     

矢量量化在语音波形编码中的应用

矢量量化(VQ)技术是近几年发展起来的一种高效数据压缩技术.本文介绍了VQ技术的发展历史、现状和它的基本原理,较为详细地讨论了基本矢量量化器的实用设计方法——LBG算法,并对原有的LBG算法进行了改进,给出了实验结果.     

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     

Quality driven gold washing adaptive vector quantization and its application to ECG data compression

The gold washing (GW) adaptive vector quantization (AVQ) (GW-AVQ) is a relatively new scheme for data compression. The adaptive nature of the algorithm provides the robustness for wide variety of the signals. However, the performance of GW-AVQ is highly dependent on a preset parameter called distortion threshold (dth) which must be determined by experience or trial-and-error. We propose an algorithm that allows us to assign an initial dth arbitrarily and then automatically progress toward a desired dth according to a specified quality criterion, such as the percent of root mean square difference (PRD) for electrocardiogram (ECG) signals. A theoretical foundation of the algorithm is also presented. This algorithm is particularly useful when multiple GW-AVQ codebooks and, thus, multiple dth's are required in a subband coding framework. Four sets of ECG data with entirely different characteristics are selected from the MIT/BIH database to verify the proposed algorithm. Both the direct GW-AVQ and a wavelet-based GW-AVQ are tested. The results show that a user specified PRD can always be reached regardless of the ECG waveforms, the initial selection of dth or whether a wavelet transform is used in conjunction with the GW-AVQ. An average result of 6% in PRD and 410 bits/s in compressed data rate is obtained with excellent visual quality.     

Combining fractal image compression and vector quantization

In fractal image compression, the code is an efficient binary representation of a contractive mapping whose unique fixed point approximates the original image. The mapping is typically composed of affine transformations, each approximating a block of the image by another block (called domain block) selected from the same image. The search for a suitable domain block is time-consuming. Moreover, the rate distortion performance of most fractal image coders is not satisfactory. We show how a few fixed vectors designed from a set of training images by a clustering algorithm accelerates the search for the domain blocks and improves both the rate-distortion performance and the decoding speed of a pure fractal coder, when they are used as a supplementary vector quantization codebook. We implemented two quadtree-based schemes: a fast top-down heuristic technique and one optimized with a Lagrange multiplier method. For the 8 bits per pixel (bpp) luminance part of the 512kappa512 Lena image, our best scheme achieved a peak-signal-to-noise ratio of 32.50 dB at 0.25 bpp.     

Error-resilient pyramid vector quantization for image compression

Pyramid vector quantization (PVQ) uses the lattice points of a pyramidal shape in multidimensional space as the quantizer codebook. It is a fixed-rate quantization technique that can be used for the compression of Laplacian-like sources arising from transform and subband image coding, where its performance approaches the optimal entropy-coded scalar quantizer without the necessity of variable length codes. In this paper, we investigate the use of PVQ for compressed image transmission over noisy channels, where the fixed-rate quantization reduces the susceptibility to bit-error corruption. We propose a new method of deriving the indices of the lattice points of the multidimensional pyramid and describe how these techniques can also improve the channel noise immunity of general symmetric lattice quantizers. Our new indexing scheme improves channel robustness by up to 3 dB over previous indexing methods, and can be performed with similar computational cost. The final fixed-rate coding algorithm surpasses the performance of typical Joint Photographic Experts Group (JPEG) implementations and exhibits much greater error resilience.     

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.     

Frame-adaptive vector quantization for image sequence coding

An adaptive technique for image sequence coding that is based on vector quantization is described. Each frame in the sequence is first decomposed into a set of vectors. A codebook is generated using the vectors of the first frame as the training sequence, and a label map is created by quantizing the vectors. The vectors of the second frame are then used to generate a new codebook, starting with the first codebook as seeds. The updated codebook is then transmitted. At the same time, the label map is replenished by coding the position and the new values of the labels that have changed from one frame to the other. The process is repeated for subsequent frames. Experimental results for a test sequences demonstrate that the technique can track the changes and maintain a nearly constant distortion over the entire sequence     

Still image coding based on vector quantization and fractalapproximation

In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is coded by fractal mapping. The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs an approximated and then decimated image as a domain pool and uses its gray patterns. Thus, the proposed algorithm utilizes fractal approximation without the constraint of contraction mapping. For approximation of an original image, we employ the discrete cosine transform (DCT) rather than conventional polynomial-based transforms. In addition, for variable blocksize segmentation, we use the fractal dimension of a block that represents the roughness of the gray surface of a region. Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.     

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。