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     

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     

An adaptive image authentication scheme for vector quantization compressed image

This paper proposes an image authentication scheme which detects illegal modifications for image vector quantization (VQ). In the proposed scheme, the index table is divided into non-overlapping index blocks. The authentication data is generated by using the pseudo random sequence. Our scheme can adaptively determine both the size of the authentication data and the number of the indices in each index block. Then, the selected indices are used to embed the secret data to generate the embedded image.To authenticate the given VQ compressed image, two sets of the authentication data are needed to perform the tamper detection process. One set is generated by using the pseudo random number sequence. The other set is extracted from the compressed image. The experimental results demonstrate that the proposed scheme achieves acceptable image quality of the embedded image while keeping good detecting accuracy.     

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。     

Variable rate vector quantization for medical image compression

Three techniques for variable-rate vector quantizer design are applied to medical images. The first two are extensions of an algorithm for optimal pruning in tree-structured classification and regression due to Breiman et al. The code design algorithms find subtrees of a given tree-structured vector quantizer (TSVQ), each one optimal in that it has the lowest average distortion of all subtrees of the TSVQ with the same or lesser average rate. Since the resulting subtrees have variable depth, natural variable-rate coders result. The third technique is a joint optimization of a vector quantizer and a noiseless variable-rate code. This technique is relatively complex but it has the potential to yield the highest performance of all three techniques.     

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).     

A complexity reduction technique for image vector quantization

A technique for reducing the complexity of spatial-domain image vector quantization (VQ) is proposed. The conventional spatial domain distortion measure is replaced by a transform domain subspace distortion measure. Due to the energy compaction properties of image transforms, the dimensionality of the subspace distortion measure can be reduced drastically without significantly affecting the performance of the new quantizer. A modified LBG algorithm incorporating the new distortion measure is proposed. Unlike conventional transform domain VQ, the codevector dimension is not reduced and a better image quality is guaranteed. The performance and design considerations of a real-time image encoder using the techniques are investigated. Compared with spatial domain a speed up in both codebook design time and search time is obtained for mean residual VQ, and the size of fast RAM is reduced by a factor of four. Degradation of image quality is less than 0.4 dB in PSNR.     

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.     

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     

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.     

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。     

Vector-based signal processing and quantization for image and videocompression

Image and video compression has become an increasingly important and active area. Many techniques have been developed in this area. Any compression technique can be modeled as a three-stage process. The first stage can be generally called a signal processing stage where an image or video signal is converted into a different domain. Usually, there is no or little loss of information in this stage. The second stage is quantization where loss of information occurs. The third stage is lossless coding that generates the compressed bit stream. The purpose of the signal processing stage is to convert an image or video signal into such a form that quantization can achieve better performance than without the signal processing stage. Because the quantization stage is the place where most of compression is achieved and loss of information occurs, it is naturally the central stage of any compression technique. Since scalar quantization or vector quantization may be used in the second stage, the operation in the first stage should be scalar-based or vector-based respectively in order to match the second stage so that the compression performance can be optimized. In this paper, we summarize the most recent research results on vector-based signal processing and quantization techniques that have shown high compression performance     

Novel watermarking scheme for image authentication using vector quantization approach

In this paper a new invisible watermarking algorithm using vector quantization (VQ) approach for image content authentication application is proposed. At the first stage, a robust verification watermark is embedded using properties of indices of vector quantized image using key based approach. At the second stage, the semi-fragile watermark is embedded by using modified index key based (MIKB) method. Robust watermark andVQenhance the security of the system by providing its double protection. The semi-fragile watermark is utilized for the authentication of the received image. Blind extraction of the watermark is performed independently in two successive steps i.e. in the order of embedding. Further, in oder to classify quantitatively an incidental or intentional attack, which at present is not being followed, a quantitative threshold approach using pixel neighborhood clustering is suggested. The existing methods employ the qualitative approach of identifying incidental or intentional attacks. Imperceptibility of watermarked image is 41 dB on the average, and it is also possible to detect and locate tamper with very high sensitivity. The present scheme is compared with the existing algorithms. The performance of proposed algorithm has been tested on various practical images. It outperforms in distinguishing malicious tampering from content preserving changes and the tampered regions are accurately localized.     

Shape-gain vector quantization for noisy channels with applicationsto image coding

A new design procedure for shape-gain vector quantizers (SGVQs) which leads to substantially improved robustness against channel errors without increasing the computational complexity is proposed. This aim is achieved by including the channel transition probabilities in the design procedure, leading to an improved assignment of binary codewords to the coding regions as well as a change of partition and centroids. In contrast to conventional design, negative gain values are also permitted. The new design procedure is applied to adaptive transform image coding. Simulation results are compared with those obtained by the conventional design procedure. The new algorithm is particularly useful for heavily distorted or fading channels     

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.     

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     

A virtual image cryptosystem based upon vector quantization

We propose a new image cryptosystem to protect image data. It encrypts the original image into another virtual image. Since both original and virtual images are significant, our new cryptosystem can confuse illegal users. Besides the camouflage, this new cryptosystem has three other benefits. First, our cryptosystem is secure even if the illegal users know that our virtual image is a camouflage. Second, this cryptosystem can compress image data. Finally, our method is more efficient than a method that encrypts the entire image directly.