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.     

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     

Hybrid vector quantization for multiresolution image coding

In this correspondence, we propose a coding scheme that exploits the redundancy of the multiresolution representation of images, in that blocks in one subimage are predicted from blocks of the adjacent lower resolution subimage with the same orientation. The pool of blocks used for prediction of a given subband plays the role of a codebook that is built from vectors of coefficients inside the subband decomposition itself. Whenever the prediction procedure does not give satisfactory results with respect to a target quality, the block coefficients are quantized using a geometric vector quantizer for a Laplacian source.     

Hierarchical finite-state vector quantization for image coding

The hierarchical finite-state vector quantization (HFSVQ) introduced in the paper is an improvement of the finite state vector quantization combined with hierarchical multirate image coding. Based on an understanding of the perception of human eye and the structural features of images, the HFSVQ technique employs different coding rates and different numbers of the predictive states for representative vector selection. The bit rate used to encode images is very low while the reconstructed images can still achieve a satisfactory perceptual quality     

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     

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.     

Predictive residual vector quantization [image coding]

This paper presents a new vector quantization technique called predictive residual vector quantization (PRVQ). It combines the concepts of predictive vector quantization (PVQ) and residual vector quantization (RVQ) to implement a high performance VQ scheme with low search complexity. The proposed PRVQ consists of a vector predictor, designed by a multilayer perceptron, and an RVQ that is designed by a multilayer competitive neural network. A major task in our proposed PRVQ design is the joint optimization of the vector predictor and the RVQ codebooks. In order to achieve this, a new design based on the neural network learning algorithm is introduced. This technique is basically a nonlinear constrained optimization where each constituent component of the PRVQ scheme is optimized by minimizing an appropriate stage error function with a constraint on the overall error. This technique makes use of a Lagrangian formulation and iteratively solves a Lagrangian error function to obtain a locally optimal solution. This approach is then compared to a jointly designed and a closed-loop design approach. In the jointly designed approach, the predictor and quantizers are jointly optimized by minimizing only the overall error. In the closed-loop design, however, a predictor is first implemented; then the stage quantizers are optimized for this predictor in a stage-by-stage fashion. Simulation results show that the proposed PRVQ scheme outperforms the equivalent RVQ (operating at the same bit rate) and the unconstrained VQ by 2 and 1.7 dB, respectively. Furthermore, the proposed PRVQ outperforms the PVQ in the rate-distortion sense with significantly lower codebook search complexity.     

Pyramidal lattice vector quantization for multiscale image coding

Introduces a new image coding scheme using lattice vector quantization. The proposed method involves two steps: biorthogonal wavelet transform of the image, and lattice vector quantization of wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, we must truncate and scale the lattice suitably. To meet this goal, we need to know how many lattice points lie within the truncated area. We investigate the case of Laplacian sources where surfaces of equal probability are spheres for the L(1) metric (pyramids) for arbitrary lattices. We give explicit generating functions for the codebook sizes for the most useful lattices like Z(n), D(n), E(s), wedge(16).     

Wavelet image coding using variable blocksize vector quantization with optimal quadtree segmentation

In this paper, we propose an image coding scheme by using the variable blocksize vector quantization (VBVQ) to compress wavelet coefficients of an image. The scheme is capable of finding an optimal quadtree segmentation of wavelet coefficients of an image for VBVQ subject to a given bit budget, such that the total distortion of quantized wavelet coefficients is minimal. From our simulation results, we can see that our proposed coding scheme has higher performance in PSNR than other wavelet/VQ or subband/VQ coding schemes.     

Video coding scheme using DCT-pyramid vector quantization

A new and effective video coding scheme for contribution quality is proposed. The CMTT/2, a joint committee of CCIR and CCITT, has proposed a video coding scheme (already approved at European level by ETS) working at 34-45 Mbit/s. Basically this proposal includes a DCT transform for spatial correlation removal and motion compensation for temporal correlation removal. The individual transform coefficients are then scalar quantized with a non uniform bit assignment. Starting from the CMTT/2 proposal, the study presents a new video coding scheme designed using a vector quantizer solution instead of the scalar one. Specifically, the pyramid vector quantization (PVQ) has been chosen as the vector quantization method as it is able to reduce the DCT coefficients Laplacian distribution. Simulation results show that the proposed video coding scheme gives the same contribution quality at 22 Mbit/s as the one obtained with the CMTT/2 proposal at 45 Mbit/s.     

Subband coding of images using vector quantization

A novel two-dimensional subband coding technique is presented that can be applied to images as well as speech. A frequency-band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 equal-rate subbands. These 16 parallel subband signals are regarded as a 16-dimensional vector source and coded as such using vector quantization. In the asymptotic case of high bit rates, a theoretical analysis yields that a lower bound to the gain is attainable by choosing this approach over scalar quantization of each subband with an optimal bit allocation. It is shown that vector quantization in this scheme has several advantages over coding the subbands separately. Experimental results are given, and it is shown the scheme has a performance that is comparable to that of more complex coding techniques     

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.     

Image coding using entropy-constrained residual vector quantization

An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relatively large vector sizes and high output rates. Experimental results indicate that good image reproduction quality can be achieved at relatively low bit rates. For example, a peak signal-to-noise ratio of 30.09 dB is obtained for the 512x512 LENA image at a bit rate of 0.145 b/p.     

Kronecker-product gain-shape vector quantization for multispectraland hyperspectral image coding

This paper proposes a new vector quantization based (VQ-based) technique for very low bit rate encoding of multispectral images. We rely on the assumption that the shape of a generic spatial block does not change significantly from band to band, as is the case for high spectral-resolution imagery. In such a hypothesis, it is possible to accurately quantize a three-dimensional (3-D) block-composed of homologous two-dimensional (2-D) blocks drawn from several bands-as the Kronecker-product of a spatial-shape codevector and a spectral-gain codevector, with significant computation saving with respect to straight VQ. An even higher complexity reduction is obtained by representing each 3-D block in its minimum-square-error Kronecker-product form and by quantizing the component shape and gain vectors. For the block sizes considered, this encoding strategy is over 100 times more computationally efficient than unconstrained VQ, and over ten times more computationally efficient than direct gain-shape VQ. The proposed technique is obviously suboptimal with respect to VQ, but the huge complexity reduction allows one to use much larger blocks than usual and to better exploit both the statistical and psychovisual redundancy of the image. Numerical experiments show fully satisfactory results whenever the shape-invariance hypothesis turns out to be accurate enough, as in the case of hyperspectral images. In particular, for a given level of complexity and image quality, the compression ratio is up to five times larger than that provided by ordinary VQ, and also larger than that provided by other techniques specifically designed for multispectral image coding.     

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。     

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.     

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.     

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.     

Image coding using vector quantization: a review

A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains