dct domain watermarking techniques for still images detector performance analysis and a new structure a spread spectrum like discrete cosine transform dct domain watermarking technique for copyright protection of still digital images is analyzed the dct is applied in blocks of 8 spl times 8 pixels as in the jpeg algorithm the watermark can encode information to track illegal misuses for flexibility purposes the original image is not necessary during the ownership verification process so it must be modeled by noise two tests are involved in the ownership verification stage watermark decoding in which the message carried by the watermark is extracted and watermark detection which decides whether a given image contains a watermark generated with a certain key we apply generalized gaussian distributions to statistically model the dct coefficients of the original image and show how the resulting detector structures lead to considerable improvements in performance with respect to the correlation receiver which has been widely considered in the literature and makes use of the gaussian noise assumption as a result of our work analytical expressions for performance measures such as the probability of errors in watermark decoding and the probabilities of false alarms and of detection in watermark detection are derived and contrasted with experimental results

receiver designs for differential uwb systems with multiple access interference most existing differential receivers for ultra wideband uwb communications employ gaussian approximation for multiple access interference mai however for a system with strong interference caused by a small number of active users significant performance degradation is found for differential uwb receivers due to the impreciseness of gaussian approximation on impulsive mai in this paper we propose new differential uwb receivers based on the generalized gaussian gg distribution which subsumes gaussian distribution and laplace distribution as special cases numerical results show that the gg distribution approximates mai well in addition we show that gg based multiple symbol differential receivers can be formulated as the same form as the conventional multiple symbol differential receivers which can be efficiently solved using existing algorithms simulations are conducted to demonstrate the superior performance of the proposed receivers to that of the existing differential uwb receivers when the interference caused by a small number of active users is strong

a pyramid vector quantizer the geometric properties of a memoryless laplacian source are presented and used to establish a source coding theorem motivated by this geometric structure a pyramid vector quantizer pvq is developed for arbitrary vector dimension the pvq is based on the cubic lattice points that lie on the surface of an l dimensional pyramid and has simple encoding and decoding algorithms a product code version of the pvq is developed and generalized to apply to a variety of sources analytical expressions are derived for the pvq mean square error mse and simulation results are presented for pvq encoding of several memoryless sources for large rate and dimension pvq encoding of memoryless laplacian gamma and gaussian sources provides rose improvements of 5 64 8 40 and 2 39 db respectively over the corresponding optimum scalar quantizer although suboptimum in a rate distortion sense because the pvq can encode large dimensional vectors it offers significant reduction in rose distortion compared with the optimum lloyd max scalar quantizer and provides an attractive alternative to currently available vector quantizers

large scale multiple kernel learning while classical kernel based learning algorithms are based on a single kernel in practice it is often desirable to use multiple kernels lanckriet et al 2004 considered conic combinations of kernel matrices for classification leading to a convex quadratically constrained quadratic program we show that it can be rewritten as a semi infinite linear program that can be efficiently solved by recycling the standard svm implementations moreover we generalize the formulation and our method to a larger class of problems including regression and one class classification experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined and helps for automatic model selection improving the interpretability of the learning result in a second part we discuss general speed up mechanism for svms especially when used with sparse feature maps as appear for string kernels allowing us to train a string kernel svm on a 10 million real world splice data set from computational biology we integrated multiple kernel learning in our machine learning toolbox shogun for which the source code is publicly available at http www fml tuebingen mpg de raetsch projects shogun

scalar quantization for estimation from an asymptotic design to a practical solution in this paper we study an asymptotic approximation of the fisher information for the estimation of a scalar parameter using quantized measurements we show that as the number of quantization intervals tends to infinity the loss of fisher information induced by quantization decreases exponentially as a function of the number of quantization bits a characterization of the optimal quantizer through its interval density and an analytical expression for the fisher information are obtained a comparison between optimal uniform and nonuniform quantization for the location and scale estimation problems shows that nonuniform quantization is only slightly better than uniform quantization as the optimal quantization intervals are shown to depend on the unknown parameters by applying adaptive algorithms that jointly estimate the parameter and set the thresholds in the location and scale estimation problems we show that the asymptotic results can be approximately reached in practice using only 4 or 5 quantization bits

multiscale modeling and estimation of poisson processes with application to photon limited imaging many important problems in engineering and science are well modeled by poisson processes in many applications it is of great interest to accurately estimate the intensities underlying observed poisson data in particular this work is motivated by photon limited imaging problems this paper studies a new bayesian approach to poisson intensity estimation based on the haar wavelet transform it is shown that the haar transform provides a very natural and powerful framework for this problem using this framework a novel multiscale bayesian prior to model intensity functions is devised the new prior leads to a simple bayesian intensity estimation procedure furthermore we characterize the correlation behavior of the new prior and show that it has 1 f spectral characteristics the new framework is applied to photon limited image estimation and its potential to improve nuclear medicine imaging is examined

robust rate control for wavelet based image coding via conditional probability models real time rate control for wavelet image coding requires characterization of the rate required to code quantized wavelet data an ideal robust solution can be used with any wavelet coder and any quantization scheme a large number of wavelet quantization schemes perceptual and otherwise are based on scalar dead zone quantization of wavelet coefficients a key to performing rate control is thus fast accurate characterization of the relationship between rate and quantization step size the r q curve a solution is presented using two invocations of the coder that estimates the slope of each r q curve via probability modeling the method is robust to choices of probability models quantization schemes and wavelet coders because of extreme robustness to probability modeling a fast approximation to spatially adaptive probability modeling can be used in the solution as well with respect to achieving a target rate the proposed approach and associated fast approximation yield average percentage errors around 0 5 and 1 0 on images in the test set by comparison 2 coding pass rho domain modeling yields errors around 2 0 and post compression rate distortion optimization yields average errors of around 1 0 at rates below 0 5 bits per pixel bpp that decrease down to about 0 5 at 1 0 bpp both methods exhibit more competitive performance on the larger images the proposed method and fast approximation approach are also similar in speed to the other state of the art methods in addition to possessing speed and accuracy the proposed method does not require any training and can maintain precise control over wavelet step sizes which adds flexibility to a wavelet based image coding system

geodesics on the manifold of multivariate generalized gaussian distributions with an application to multicomponent texture discrimination we consider the rao geodesic distance gd based on the fisher information as a similarity measure on the manifold of zero mean multivariate generalized gaussian distributions mggd the mggd is shown to be an adequate model for the heavy tailed wavelet statistics in multicomponent images such as color or multispectral images we discuss the estimation of mggd parameters using various methods we apply the gd between mggds to color texture discrimination in several classification experiments taking into account the correlation structure between the spectral bands in the wavelet domain we compare the performance both in terms of texture discrimination capability and computational load of the gd and the kullback leibler divergence kld likewise both uni and multivariate generalized gaussian models are evaluated characterized by a fixed or a variable shape parameter the modeling of the interband correlation significantly improves classification efficiency while the gd is shown to consistently outperform the kld as a similarity measure

a statistical inter cell interference model for downlink cellular ofdma networks under log normal shadowing and multipath rayleigh fading the probability density function pdf of the intercell interference for downlink cellular orthogonal frequency division multiple access networks under log normal shadowing and multipath rayleigh fading is derived the derived pdf is observed to highly deviate from the gaussian distribution especially at moderate to small network loads based on the derived inter cell interference statistics the decoder performance can be significantly improved compared to the case when the traditional decoder optimized for the awgn channel is employed a simple suboptimum decoding algorithm that closely approximates the derived optimum decoder performance is also proposed

fuzzy dea a perceptual evalution method abstract dea data envelopment analysis is a non parametric technique for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs in fact in a real evaluation problem input and output data of entities evaluated often fluctuate these fluctuating data can be represented as linguistic variables characterized by fuzzy numbers for reflecting a kind of general feeling or experience of experts based on the fundamental ccr model a fuzzy dea model is proposed to deal with the efficiency evaluation problem with the given fuzzy input and output data furthermore an extension of the fuzzy dea model to a more general form is also proposed with considering the relationship between dea and ra regression analysis using the proposed fuzzy dea models the crisp efficiency in ccr model is extended to be a fuzzy number to reflect the inherent uncertainty in real evaluation problems

analysis of multiresolution image denoising schemes using generalized gaussian and complexity priors research on universal and minimax wavelet shrinkage and thresholding methods has demonstrated near ideal estimation performance in various asymptotic frameworks however image processing practice has shown that universal thresholding methods are outperformed by simple bayesian estimators assuming independent wavelet coefficients and heavy tailed priors such as generalized gaussian distributions ggds in this paper we investigate various connections between shrinkage methods and maximum a posteriori map estimation using such priors in particular we state a simple condition under which map estimates are sparse we also introduce a new family of complexity priors based upon rissanen s universal prior on integers one particular estimator in this class outperforms conventional estimators based on earlier applications of the minimum description length mdl principle we develop analytical expressions for the shrinkage rules implied by ggd and complexity priors this allows us to show the equivalence between universal hard thresholding map estimation using a very heavy tailed ggd and mdl estimation using one of the new complexity priors theoretical analysis supported by numerous practical experiments shows the robustness of some of these estimates against mis specifications of the prior a basic concern in image processing applications

a taxonomy and review of the fuzzy data envelopment analysis literature two decades in the making data envelopment analysis dea is a methodology for measuring the relative efficiencies of a set of decision making units dmus that use multiple inputs to produce multiple outputs crisp input and output data are fundamentally indispensable in conventional dea however the observed values of the input and output data in real world problems are sometimes imprecise or vague many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in dea in this study we provide a taxonomy and review of the fuzzy dea methods we present a classification scheme with four primary categories namely the tolerance approach the a level based approach the fuzzy ranking approach and the possibility approach we discuss each classification scheme and group the fuzzy dea papers published in the literature over the past 20 years to the best of our knowledge this paper appears to be the only review and complete source of references on fuzzy dea

a complex generalized gaussian distribution characterization generation and estimation the generalized gaussian distribution ggd provides a flexible and suitable tool for data modeling and simulation however the characterization of the complex valued ggd in particular generation of samples from a complex ggd have not been well defined in the literature in this correspondence we provide a thorough presentation of the complex valued ggd by i constructing the probability density function pdf ii defining a procedure for generating random numbers from the complex valued ggd and iii implementing a maximum likelihood estimation mle procedure for the shape and covariance parameters in the complex domain we quantify the performance of the mle with simulations and actual radar data

unary prefixed huffman coding for a group of quantized generalized gaussian sources we propose a coding algorithm called unary prefixed huffman uph coding for a family of infinite sources called quantized generalized gaussian gg sources compared with the existing codes for these gg sources the uph algorithm provides a more adaptive approach and its coding efficiency is upper bounded by entropy 2

stabilizing nonuniformly quantized compressed sensing with scalar companders this paper addresses the problem of stably recovering sparse or compressible signals from compressed sensing measurements that have undergone optimal nonuniform scalar quantization i e minimizing the common l2 norm distortion generally this quantized compressed sensing qcs problem is solved by minimizing the l1 norm constrained by the l2 norm distortion in such cases remeasurement and quantization of the reconstructed signal do not necessarily match the initial observations showing that the whole qcs model is not consistent our approach considers instead that quantization distortion more closely resembles heteroscedastic uniform noise with variance depending on the observed quantization bin generalizing our previous work on uniform quantization we show that for nonuniform quantizers described by the compander formalism quantization distortion may be better characterized as having bounded weighted lp norm p 2 for a particular weighting we develop a new reconstruction approach termed generalized basis pursuit denoise gbpdn which minimizes the l1 norm of the signal to reconstruct constrained by this weighted lp norm fidelity we prove that for standard gaussian sensing matrices and k sparse or compressible signals in rn with at least k logn k p 2 measurements i e under strongly oversampled qcs scenario gbpdn is l2 l1 instance optimal and stable recovers all such sparse or compressible signals the reconstruction error decreases as o 2 b p 1 given a budget of b bits per measurement this yields a reduction by a factor p 1 of the reconstruction error compared to the one produced by l2 norm constrained decoders we also propose an primal dual proximal splitting scheme to solve the gbpdn program which is efficient for large scale problems interestingly extensive simulations testing the gbpdn effectiveness confirm the trend predicted by the theory that the reconstruction error can indeed be reduced by increasing p but this is achieved at a much less stringent oversampling regime than the one expected by the theoretical bounds besides the qcs scenario we also show that gbpdn applies straightforwardly to the related case of cs measurements corrupted by heteroscedastic generalized gaussian noise with provable reconstruction error reduction

efficient chi 2 kernel linearization via random feature maps explicit feature mapping is an appealing way to linearize additive kernels such as chi 2 kernel for training large scale support vector machines svms although accurate in approximation feature mapping could pose computational challenges in high dimensional settings as it expands the original features to a higher dimensional space to handle this issue in the context of chi 2 kernel svms learning we introduce a simple yet efficient method to approximately linearize chi 2 kernel through random feature maps the main idea is to use sparse random projection to reduce the dimensionality of feature maps while preserving their approximation capability to the original kernel we provide approximation error bound for the proposed method furthermore we extend our method to chi 2 multiple kernel svms learning extensive experiments on large scale image classification tasks confirm that the proposed approach is able to significantly speed up the training process of the chi 2 kernel svms at almost no cost of testing accuracy

parameter estimation for multivariate generalized gaussian distributions due to its heavy tailed and fully parametric form the multivariate generalized gaussian distribution mggd has been receiving much attention in signal and image processing applications considering the estimation issue of the mggd parameters the main contribution of this paper is to prove that the maximum likelihood estimator mle of the scatter matrix exists and is unique up to a scalar factor for a given shape parameter 0 1 moreover an estimation algorithm based on a newton raphson recursion is proposed for computing the mle of mggd parameters various experiments conducted on synthetic and real data are presented to illustrate the theoretical derivations in terms of number of iterations and number of samples for different values of the shape parameter the main conclusion of this work is that the parameters of mggds can be estimated using the maximum likelihood principle with good performance

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 sup 1 metric pyramids for arbitrary lattices we give explicit generating functions for the codebook sizes for the most useful lattices like z sup n d sub n e sub s spl and sub 16

lightweight probabilistic texture retrieval this paper contemplates the framework of probabilistic image retrieval in the wavelet domain from a computational point of view we not only focus on achieving high retrieval rates but also discuss possible performance bottlenecks which might prevent practical application we propose a novel retrieval approach which is motivated by previous research work on modeling the marginal distributions of wavelet transform coefficients the building blocks of our work are the dual tree complex wavelet transform and a number of statistical models for the coefficient magnitudes image similarity measurement is accomplished by using closed form solutions for the kullback leibler divergences between the statistical models we provide an in depth computational analysis regarding the number of arithmetic operations required for similarity measurement and model parameter estimation the experimental retrieval results on a widely used texture image database show that we achieve competitive retrieval results at low computational cost

decision theoretic saliency computational principles biological plausibility and implications for neurophysiology and psychophysics a decision theoretic formulation of visual saliency first proposed for top down processing object recognition gao vasconcelos 2005a is extended to the problem of bottom up saliency under this formulation optimality is defined in the minimum probability of error sense under a constraint of computational parsimony the saliency of the visual features at a given location of the visual field is defined as the power of those features to discriminate between the stimulus at the location and a null hypothesis for bottom up saliency this is the set of visual features that surround the location under consideration discrimination is defined in an information theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images it is shown that under the assumption that saliency is driven by linear filtering the optimal detector consists of what is usually referred to as the standard architecture of v1 a cascade of linear filtering divisive normalization rectification and spatial pooling the optimal detector is also shown to replicate the fundamental properties of the psychophysics of saliency stimulus pop out saliency asymmetries for stimulus presence versus absence disregard of feature conjunctions and weber s law finally it is shown that the optimal saliency architecture can be applied to the solution of generic inference problems in particular for the class of stimuli studied it performs the three fundamental operations of statistical inference assessment of probabilities implementation of bayes decision rule and feature selection

asymptotically optimal detection for additive watermarking in the dct and dwt domains most of the watermarking schemes that have been proposed until now employ a correlation detector matched filter the current paper proposes a new detector scheme that can be applied in the case of additive watermarking in the dct discrete cosine transform or dwt discrete wavelet transform domain certain properties of the probability density function of the coefficients in these domains are exploited thus an asymptotically optimal detector is constructed based on well known results of the detection theory experimental results prove the superiority of the proposed detector over the correlation detector

a globally convergent and consistent method for estimating the shape parameter of a generalized gaussian distribution we propose a novel methodology for estimating the shape parameter of a generalized gaussian distribution ggd this new method is based on a simple estimating equation derived from a convex shape equation the estimating equation is completely independent of gamma and polygamma functions thus no lookup table interpolation or additional subroutine to evaluate these functions are required for real time implementations of the proposed method which is in contrast to all existing methods furthermore we establish that the shape equation has a unique global root on the positive real line and the newton raphson root finding algorithm converges to the unique global root from any starting point in a semi infinite interval thetamin more importantly we show that the sample based shape estimating equation has a unique global root with probability tending to one and the root is consistent for the true shape parameter finally we prove via fixed point arguments that with probability tending to one the newton raphson algorithm converges to the unique global root of the sample shape estimating equation from any starting point in thetamin some numerical experiments are also provided to demonstrate the global convergence and the excellent finite sample performance of the proposed method

asymptotic relative efficiency and exact variance stabilizing transformation for the generalized gaussian distribution it is demonstrated that the sampling distributions of the maximum likelihood ml estimator and its studentized statistic for the generalized gaussian distribution do not pass the most powerful normality tests even for fairly large sample sizes this disagreement with what the standard large sample ml theory predicts and the computational burden of having to deal with its associated polygamma functions motivate the consideration of a competing convexity based estimator the asymptotic normality of this estimator is derived it is shown that the competing estimator is almost as efficient as the ml estimator and its asymptotic relative efficiency to the ml estimator is equal to 1 in the limit as the shape parameter approaches zero more important its asymptotic distribution admits an exact variance stabilizing transformation whereas the asymptotic variance function of the ml estimator does not have a closed form variance stabilizing transformation the exact transformation is a composition of the inverse hyperbolic cotangent and square root functions besides stabilizing the variance the inverse hyperbolic cotangent and square root transformation is remarkably effective for symmetrizing and normalizing the sampling distribution of the estimator and hence improving the standard normal approximation furthermore this simple transformation provides a quite accurate approximation to the non closed form variance stabilizing transformation of the ml estimator

structured prefix codes for quantized low shape parameter generalized gaussian sources the highly peaked wide tailed pdf s that are encountered in many image coding algorithms are often modeled using the family of generalized gaussian gg pdf s we study entropy coding of quantized gg sources using prefix codes that are highly structured and which therefore involve low computational complexity we provide bounds for the redundancy associated with applying these codes to quantized gg sources we also explore code efficiency and code choice for a wide range of gg source and quantizer parameters

a new ranking method to fuzzy data envelopment analysis due to its wide practical use data envelopment analysis dea has been adapted to many fields to deal with problems that have occurred in practice one adaptation has been in the field of ranking decision making units dmus most methods of ranking dmus assume that all input and output data are exactly known but in real life the data cannot be precisely measured thus this paper will carry out some researches to dea under fuzzy environment a fuzzy comparison of fuzzy variables is defined and the ccr model is extended to be a fuzzy dea model based on credibility measure in order to rank all the dmus a full ranking method will be given since the ranking method involves a fuzzy function a fuzzy simulation is designed and embedded into the genetic algorithm to establish a hybrid intelligent algorithm however it is shown to be possible to avoid some of the need for dealing with these nonlinear problems by identifying conditions under which they can be replaced by linear problems finally we will provide a numerical example to illustrate the fuzzy dea model and the ranking method

expected value of fuzzy variable and fuzzy expected value models this paper will present a novel concept of expected values of fuzzy variables which is essentially a type of choquet integral and coincides with that of random variables in order to calculate the expected value of general fuzzy variable a fuzzy simulation technique is also designed finally we construct a spectrum of fuzzy expected value models and integrate fuzzy simulation neural network and genetic algorithms to produce a hybrid intelligent algorithm for solving general fuzzy expected value models

linear regression with random projections we investigate a method for regression that makes use of a randomly generated subspace gp f of finite dimension p of a given large possibly infinite dimensional function space f for example l2 0 1 d r gp is defined as the span of p random features that are linear combinations of a basis functions of f weighted by random gaussian i i d coefficients we show practical motivation for the use of this approach detail the link that this random projections method share with rkhs and gaussian objects theory and prove both in deterministic and random design approximation error bounds when searching for the best regression function in gp rather than in f and derive excess risk bounds for a specific regression algorithm least squares regression in gp this paper stresses the motivation to study such methods thus the analysis developed is kept simple for explanations purpose and leaves room for future developments

image coding with uniform and piecewise uniform vector quantizers new lattice vector quantizer design procedures for nonuniform sources that yield excellent performance while retaining the structure required for fast quantization are described analytical methods for truncating and scaling lattices to be used in vector quantization are given and an analytical technique for piecewise linear multidimensional companding is presented the uniform and piecewise uniform lattice vector quantizers are then used to quantize the discrete cosine transform coefficients of images and their objective and subjective performance and complexity are contrasted with other lattice vector quantizers and with lbg training mode designs

fuzzy data envelopment analysis dea a possibility approach abstract evaluating the performance of activities or organizations by traditional data envelopment analysis dea models requires crisp input output data however in real world problems inputs and outputs are often imprecise this paper develops dea models using imprecise data represented by fuzzy sets i e fuzzy dea models it is shown that fuzzy dea models take the form of fuzzy linear programming which typically are solved with the aid of some methods to rank fuzzy sets as an alternative a possibility approach is introduced in which constraints are treated as fuzzy events the approach transforms fuzzy dea models into possibility dea models by using possibility measures of fuzzy events fuzzy constraints we show that for the special case in which fuzzy membership functions of fuzzy data are of trapezoidal types possibility dea models become linear programming models a numerical experiment is used to illustrate the approach and compare the results with those obtained with alternative approaches

robust gaussian and non gaussian matched subspace detection we address the problem of matched filter and subspace detection in the presence of arbitrary noise and interference or interfering signals that may lie in an arbitrary unknown subspace of the measurement space a minmax methodology developed to deal with this uncertainty can also be adapted to situations where partial information on the interference or other uncertainties is available this methodology leads to a hypothesis test with adequate levels of false alarm robustness and signal detection sensitivity the robust test is applicable to a large class of noise density functions in addition generalized likelihood ratio glr detectors are derived for the class of generalized gaussian noise the detectors are generalizations of the spl chi sup 2 t and f statistics used with gaussian noise which are themselves motivated in a new way by the robust test for matched filter detection these expressions are simpler and computationally efficient the robust test reduces to the conventional test when unlearned subspace interference is known to be absent the results demonstrate that when compared with the conventional detector the robust one trades off some detection performance in the absence of interference for the sake of robustness in its presence

robust multidimensional matched subspace classifiers based on weighted least squares we propose and design two classes of robust subspace classifiers for classification of multidimensional signals our classifiers are based on robust m estimators and the least median of squares principle and we show that they may be unified as iterated reweighted oblique subspace classifiers the performance of the proposed classifiers are demonstrated by two examples noncoherent detection of space time frequency shift keying signals and shape classification of partially occluded two dimensional 2 d objects in both cases the proposed robust subspace classifiers outperform the conventional subspace classifiers

fuzzy model based clustering and its application in image segmentation a fuzzy generalized gaussian density ggd segmentation model is proposed a ggd based agglomerative fuzzy algorithm is developed a two stage fuzzy ggd segmentation algorithm is proposed this paper proposes a novel fuzzy model based unsupervised learning algorithm with boundary correction for image segmentation we propose a fuzzy generalized gaussian density ggd segmentation model and the ggd based agglomerative fuzzy algorithm for grouping image pixels the merits of algorithm are that it is not sensitive to initial parameters and that the number of groups can be estimated via the validation technique to minimize the objective function of the model we define a dissimilarity measure based on the kullbackleibler divergence of the ggds that computes the discrepancy between ggds in the space of generalized probability distributions to effectively segment images with various textures we propose a two stage fuzzy ggd segmentation algorithm the first stage adopts the proposed fuzzy algorithm to obtain initial segmentation and the second stage improves initial segmentation by image boundary correction experimental results show that our proposed method has a promising performance compared with existing approaches

distributions of the two dimensional dct coefficients for images for a two dimensional discrete cosine transform dct image coding system there have been different assumptions concerning the distributions of the transform coefficients this paper presents results of distribution tests that indicate that for many images the statistics of the coefficients are best approximated by a gaussian distribution for the dc coefficient and a laplacian distribution for the other coefficients furthermore from a simulation of the dct coding system it is shown that the assumption that the coefficients are laplacian yields a higher actual output signal to noise ratio and a much better agreement between theory and simulation than the gaussian assumption

optimum quantizer performance for a class of non gaussian memoryless sources the performance of optimum quantizers subject to an entropy constraint is studied for a wide class of memoryless sources for a general distortion criterion necessary conditions are developed for optimality and a recursive algorithm is described for obtaining the optimum quantizer under a mean square error criterion the performance of entropy encoded uniform quantization of memoryless gaussian sources is well known to be within 0 255 bits sample of the rate distortion bound at relatively high rates despite claims to the contrary it is demonstrated that similar performance can be expected for a wide range of memoryless sources indeed for the cases considered the worst case performance is observed to be less than 0 3 bits sample from the rate distortion bound and in most cases this disparity is less at iow rates

parametric generalized gaussian density estimation the primary objective of this paper is to compare the large sample as well as the small sample properties of different methods for estimating the parameters of a three parameter generalized gaussian distribution three estimators namely the moment method mm the maximum likelihood ml and the moment newton step mns estimators are considered the applicability of general asymptotic optimality results of the efficient ml and mns estimation techniques is studied in the generalized gaussian context the asymptotic normal distributions of the estimators are obtained the asymptotic relative superiority of the ml estimator or its variant the mns estimator over the moment method is studied in terms of asymptotic relative efficiency based on this study it is concluded that deviations from normality in the underlying distribution of the data necessitate the use of the efficient ml or mns methods in the small sample case a detailed comparative study of the estimators is made possible by extensive monte carlo simulations from this study it is concluded that the maximum likelihood method is found to be significantly superior for heavy tailed distributions in a region of the parameter space corresponding to the vicinity of the gaussian distribution the moment method compares well with the other methods further the mns estimator is shown to perform best for light tailed distributions the simulation results are shown to lend support to analytically derived asymptotic results for each of the methods

a procedure for ranking efficient units in data envelopment analysis data envelopment analysis dea evaluates the relative efficiency of decision making units dmus but does not allow for a ranking of the efficient units themselves a modified version of dea based upon comparison of efficient dmus relative to a reference technology spanned by all other units is developed the procedure provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods

linear worst case estimators for denoising quantization noise in transform coded images transform coded images exhibit distortions that fall outside of the assumptions of traditional denoising techniques in this paper we use tools from robust signal processing to construct linear worst case estimators for the denoising of transform compressed images we show that while standard denoising is fundamentally determined by statistical models for images alone the distortions induced by transform coding are heavily dependent on the structure of the transform used our method thus uses simple models for the image and for the quantization error with the latter capturing the transform dependency based on these models we derive optimal linear estimators of the original image that are optimal in the mean squared error sense for the worst case cross correlation between the original and the quantization error our construction is transform agnostic and is applicable to transforms from block discrete cosine transforms to wavelets furthermore our approach is applicable to different types of image statistics and can also serve as an optimization tool for the design of transforms quantizers through the interaction of the source and quantizer models our work provides useful insights and is instrumental in identifying and removing quantization artifacts from general signals coded with general transforms as we decouple the modeling and processing steps we allow for the construction of many different types of estimators depending on the desired sophistication and available computational complexity in the low end of this spectrum our lookup table based estimator which can be deployed in low complexity environments provides competitive psnr values with some of the best results in the literature

optimal differential energy watermarking of dct encoded images and video this paper proposes the differential energy watermarking dew algorithm for jpeg mpeg streams the dew algorithm embeds label bits by selectively discarding high frequency discrete cosine transform dct coefficients in certain image regions the performance of the proposed watermarking algorithm is evaluated by the robustness of the watermark the size of the watermark and the visual degradation the watermark introduces these performance factors are controlled by three parameters namely the maximal coarseness of the quantizer used in pre encoding the number of dct blocks used to embed a single watermark bit and the lowest dct coefficient that we permit to be discarded we follow a rigorous approach to optimizing the performance and choosing the correct parameter settings by developing a statistical model for the watermarking algorithm using this model we can derive the probability that a label bit cannot be embedded the resulting model can be used for instance for maximizing the robustness against re encoding and for selecting adequate error correcting codes for the label bit string

lightweight detection of additive watermarking in the dwt domain this article aims at lightweight blind detection of additive spread spectrum watermarks in the dwt domain we focus on two host signal noise models and two types of hypothesis tests for watermark detection as a crucial point of our work we take a closer look at the computational requirements of watermark detectors this involves the computation of the detection response parameter estimation and threshold selection we show that by switching to approximate host signal parameter estimates or even fixed parameter settings we achieve a remarkable improvement in runtime performance without sacrificing detection performance our experimental results on a large number of images confirm the assumption that there is not necessarily a tradeoff between computation time and detection performance

efficient additive kernels via explicit feature maps large scale nonlinear support vector machines svms can be approximated by linear ones using a suitable feature map the linear svms are in general much faster to learn and evaluate test than the original nonlinear svms this work introduces explicit feature maps for the additive class of kernels such as the intersection hellinger s and 2 kernels commonly used in computer vision and enables their use in large scale problems in particular we 1 provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels 2 derive corresponding approximate finite dimensional feature maps based on a spectral analysis and 3 quantify the error of the approximation showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as 2 we demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train test times of svms we also compare with two other approximation methods nystrom s approximation of perronnin et al 1 which is data dependent and the explicit map of maji and berg 2 for the intersection kernel which as in the case of our approximations is data independent the approximations are evaluated on a number of standard data sets including caltech 101 3 daimler chrysler pedestrians 4 and inria pedestrians 5

subset selection in noise based on diversity measure minimization we develop robust methods for subset selection based on the minimization of diversity measures a bayesian framework is used to account for noise in the data and a maximum a posteriori map estimation procedure leads to an iterative procedure which is a regularized version of the focal underdetermined system solver focuss algorithm the convergence of the regularized focuss algorithm is established and it is shown that the stable fixed points of the algorithm are sparse we investigate three different criteria for choosing the regularization parameter quality of fit sparsity criterion l curve the l curve method as applied to the problem of subset selection is found not to be robust and we propose a novel modified l curve procedure that solves this problem each of the regularized focuss algorithms is evaluated through simulation of a detection problem and the results are compared with those obtained using a sequential forward selection algorithm termed orthogonal matching pursuit omp in each case the regularized focuss algorithm is shown to be superior to the omp in noisy environments

wavelet based texture retrieval using generalized gaussian density and kullback leibler distance we present a statistical view of the texture retrieval problem by combining the two related tasks namely feature extraction fe and similarity measurement sm into a joint modeling and classification scheme we show that using a consistent estimator of texture model parameters for the fe step followed by computing the kullback leibler distance kld between estimated models for the sm step is asymptotically optimal in term of retrieval error probability the statistical scheme leads to a new wavelet based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized gaussian density ggd and on the existence a closed form for the kld between ggds the proposed method provides greater accuracy and flexibility in capturing texture information while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates e g from 65 to 77 compared with traditional approaches while it retains comparable levels of computational complexity

lossless image compression by quantization feedback in a content driven enhanced laplacian pyramid in this paper the effects of quantization noise feedback on the entropy of laplacian pyramids are investigated this technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper bounded near lossless coding and therefore allows reversible compression the entropy minimizing optimum quantizer is obtained by modeling the first order distributions of the differential signals as laplacian densities and by deriving a model for the equivalent memoryless entropy a novel approach based on an enhanced laplacian pyramid is proposed for the compression either lossless or lossy of gray scale images major details are prioritized through a content driven decision rule embedded in a uniform threshold quantizer with noise feedback lossless coding shows improvements over reversible joint photographers expert group jpeg and the reduced difference pyramid schemes while lossy coding outperforms jpeg with a significant peak signal to noise ratio psnr gain also subjective quality is higher even at very low bit rates due to the absence of the annoying impairments typical of jpeg moreover image versions having resolution and snr that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on as intermediate steps of the decoding procedure without any additional cost

numerical evaluation of the lambert w function and application to generation of generalized gaussian noise with exponent 1 2 we address the problem of synthesizing a generalized gaussian noise with exponent 1 2 by means of a nonlinear memoryless transformation applied to a uniform noise we show that this transformation is expressable in terms of a special function known under the name of the lambert w function we review the main methods for numerical evaluation of the relevant branch of the multivalued lambert w function with controlled accuracy and complement them with an original rational function approximation based on these methods synthesis of the generalized gaussian noise can be performed with arbitrary accuracy we construct a simple and fast evaluation algorithm with prescribed accuracy which is especially suited for monte carlo simulation requiring large numbers of realizations of the generalized gaussian noise

recent advances of stretched gaussian distribution underlying hausdorff fractal distance and its applications in fitting stretched gaussian noise abstract this paper summarizes the latest advances of the third author s research group on stretched gaussian distribution underlying the hausdorff fractal theory and its applications in fitting stretched gaussian noise firstly the hausdorff fractal metrics are introduced as an extension of non euclidean distance based on the fractal scaling the hausdorff derivative is derived which can describe the problems of interest to construct non integer differential equations secondly we introduce the hausdorff derivatives in tackling partial differential equations and the fundamental solution of hausdorff derivative diffusion equation is stretched gaussian distribution thirdly we analyze the feasibility of the least square method for stretched gaussian noise which obeys stretched gaussian distribution the least square method is inapplicable when the noise level is larger than 5 finally by using the hausdorff fractal distance we introduce the stretched least square method which improves the traditional least square method to fit stretched gaussian noise

fast adaptive wavelet packet image compression wavelets are ill suited to represent oscillatory patterns rapid variations of intensity can only be described by the small scale wavelet coefficients which are often quantized to zero even at high bit rates our goal is to provide a fast numerical implementation of the best wavelet packet algorithm in order to demonstrate that an advantage can be gained by constructing a basis adapted to a target image emphasis is placed on developing algorithms that are computationally efficient we developed a new fast two dimensional 2 d convolution decimation algorithm with factorized nonseparable 2 d filters the algorithm is four times faster than a standard convolution decimation an extensive evaluation of the algorithm was performed on a large class of textured images because of its ability to reproduce textures so well the wavelet packet coder significantly out performs one of the best wavelet coder on images such as barbara and fingerprints both visually and in term of psnr

subband coding of images subband coding has become quite popular for the source encoding of speech this paper presents a simple yet efficient extension of this concept to the source coding of images we specify the constraints for a set of two dimensional quadrature mirror filters qmf s for a particular frequency domain partition and show that these constraints are satisfied by a separable combination of one dimensional qmf s bits are then optimally allocated among the subbands to minimize the mean squared error for dpcm coding of the subbands also an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask optimum quantization is employed with quantizers matched to the laplacian distribution subband coded images are presented along with their signal to noise ratios snr s the snr performance of the subband coder is compared to that of the adaptive discrete cosine transform dct vector quantization and differential vector quantization for bit rates of 0 67 1 0 and 2 0 bits per pixel for 256 256 monochrome images the adaptive subband coder has the best snr performance

motion compensated 3 d subband coding of video this paper describes a video coding system based on motion compensated three dimensional 3 d subband wavelet coding mc 3dsbc which can overcome the limits of both 3 d sbc and mc prediction based coding in this new system spatio temporal subbands are generated by mc temporal analysis and a spatial wavelet transform and then encoded by 3 d subband finite state scalar quantization 3dsb fssq the rate allocation from the gop level to each class of subbands is optimized by utilizing the structural property of mc 3dsbc that additive superposition approximately holds for both rate and distortion the proposed video coding system is applied to several test video clips its performance exceeds that of both a known mpeg 1 implementation and a similar subband mc predictive coder while maintaining modest computational complexity and memory size

fuzzy sets as a basis for a theory of possibility the theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable more specifically if f is a fuzzy subset of a universe of discourse u u which is characterized by its membership function f then a proposition of the form x is f where x is a variable taking values in u induces a possibility distribution tx which equates the possibility of x taking the value u to f u the compatibility of u with f in this way x becomes a fuzzy variable which is associated with the possibility distribution tx in much the same way as a random variable is associated with a probability distribution in general a variable may be associated both with a possibility distribution and a probability distribution with the weak connection between the two expressed as the possibility probability consistency principle a thesis advanced in this paper is that the imprecision that is intrinsic in natural languages is in the main possibilistic rather than probabilistic in nature thus by employing the concept of a possibility distribution a proposition p in a natural language may be translated into a procedure which computes the probability distribution of a set of attributes which are implied by p several types of conditional translation rules are discussed and in particular a translation rule for propositions of the form x is f is possible where is a number in the interval 0 1 is formulated and illustrated by examples

on the modeling of small sample distributions with generalized gaussian density in a maximum likelihood framework the modeling of sample distributions with generalized gaussian density ggd has received a lot of interest most papers justify the existence of ggd parameters through the asymptotic behavior of some mathematical expressions i e the sample is supposed to be large in this paper we show that the computation of ggd parameters on small samples is not the same as on larger ones in a maximum likelihood framework we exhibit a necessary and sufficient condition for the existence of the parameters we derive an algorithm to compute them and then compare it to some existing methods on random images of different sizes

enumeration encoding and decoding algorithms for pyramid cubic lattice and trellis codes a pyramid source code is a code that assigns equal length binary strings to all reproduction codevectors of equal weighted spl epsiv sub 1 norm a pyramid source encoding is partitioned into two concatenated mappings the first from source word to reproduction codeword within a codebook the second from the reproduction codevector to a binary string the first mapping allows distortion and is accomplished using lattice quantization or trellis coded quantization the second mapping is noiseless and is denoted as enumeration efficient pyramid enumeration encoding and decoding algorithms are presented for use with fixed rate or variable rate pyramid lattice and trellis codes

the estimation of laplace random vectors in additive white gaussian noise this paper develops and compares the maximum a posteriori map and minimum mean square error mmse estimators for spherically contoured multivariate laplace random vectors in additive white gaussian noise the mmse estimator is expressed in closed form using the generalized incomplete gamma function we also find a computationally efficient yet accurate approximation for the mmse estimator in addition this paper develops an expression for the mse for any estimator of spherically contoured multivariate laplace random vectors in additive white gaussian noise awgn the development of which again depends on the generalized incomplete gamma function the estimators are motivated and tested on the problem of wavelet based image denoising

efficient entropy estimation based on doubly stochastic models for quantized wavelet image data under a rate constraint wavelet based image coding involves strategic discarding of information such that the remaining data can be described with a given amount of rate in a practical coding system this task requires knowledge of the relationship between quantization step size and compressed rate for each group of wavelet coefficients the r q curve a common approach to this problem is to fit each subband with a scalar probability distribution and compute entropy estimates based on the model this approach is not effective at rates below 1 0 bits per pixel because the distributions of quantized data do not reflect the dependencies in coefficient magnitudes these dependencies can be addressed with doubly stochastic models which have been previously proposed to characterize more localized behavior though there are tradeoffs between storage computation time and accuracy using a doubly stochastic generalized gaussian model it is demonstrated that the relationship between step size and rate is accurately described by a low degree polynomial in the logarithm of the step size based on this observation an entropy estimation scheme is presented which offers an excellent tradeoff between speed and accuracy after a simple data gathering step estimates are computed instantaneously by evaluating a single polynomial for each group of wavelet coefficients quantized with the same step size these estimates are on average within 3 of a desired target rate for several of state of the art coders

on the modeling of dct and subband image data for compression image subband and discrete cosine transform coefficients are modeled for efficient quantization and noiseless coding quantizers and codes are selected based on laplacian fixed generalized gaussian and adaptive generalized gaussian models the quantizers and codes based on the adaptive generalized gaussian models are always superior in mean squared error distortion performance but generally by no more than 0 08 bit pixel compared with the much simpler laplacian model based quantizers and noiseless codes this provides strong motivation for the selection of pyramid codes for transform and subband image coding

wavelet based level set evolution for classification of textured images we present a supervised classification model based on a variational approach this model is specifically devoted to textured images we want to get a partition of an image composed of texture regions separated by regular interfaces each kind of texture defines a class we use a wavelet packet transform to analyze the textures characterized by their energy distribution in each sub band in order to have an image segmentation according to the classes we model the regions and their interfaces by level set functions we define a functional on these level sets whose minimizers define the optimal classification according to texture a system of coupled pdes is deduced from the functional by solving this system each region evolves according to its wavelet coefficients and interacts with the neighbor regions in order to obtain a partition with regular contours experiments are shown on synthetic and real images

support vector machines for histogram based image classification traditional classification approaches generalize poorly on image classification tasks because of the high dimensionality of the feature space this paper shows that support vector machines svm can generalize well on difficult image classification problems where the only features are high dimensional histograms heavy tailed rbf kernels of the form k x y e sup spl rho spl sigma sub i sup xia yia b with a spl les 1 and b spl les 2 are evaluated on the classification of images extracted from the corel stock photo collection and shown to far outperform traditional polynomial or gaussian radial basis function rbf kernels moreover we observed that a simple remapping of the input x sub i spl rarr x sub i sup a improves the performance of linear svm to such an extend that it makes them for this problem a valid alternative to rbf kernels

texture classification using refined histogram in this correspondence we propose a novel efficient and effective refined histogram rh for modeling the wavelet subband detail coefficients and present a new image signature based on the rh model for supervised texture classification our rh makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients and the concatenation of the rh model parameters for all wavelet subbands forms the so called rh signature to justify the usefulness of the rh signature we discuss and investigate some of its statistical properties these properties would clarify the sufficiency of the signature to characterize the wavelet subband information in addition we shall also present an efficient rh signature extraction algorithm based on the coefficient counting technique which helps to speed up the overall classification system performance we apply the rh signature to texture classification using the well known databases experimental results show that our proposed rh signature in conjunction with the use of symmetrized kullback leibler divergence gives a satisfactory classification performance compared with the current state of the art methods

statistical properties of bit plane probability model and its application in supervised texture classification the modeling of wavelet subband histograms via the product bernoulli distributions pbd has received a lot of interest and the pbd model has been applied successfully in texture image retrieval in order to fully understand the usefulness and effectiveness of the pbd model and its associated signature namely the bit plane probability bp signature on image processing applications we discuss and investigate some of their statistical properties these properties would help to clarify the sufficiency of the bp signature to characterize wavelet subbands which in turn justifies its use in real time applications we apply the bp signature on supervised texture classification problem and experimental results suggest that the weighted l1 norm rather than the standard l1 norm should be used for the bp signature comparative classification experiments show that our method outperforms the current state of the art generalized gaussian density approaches

image segmentation using fuzzy region competition and spatial frequency information this paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation in the proposed energy functional each region is represented by a fuzzy membership function and a data fidelity term that measures the conformity of spatial and frequency data within each region to generalized gaussian densities whose parameters are determined jointly with the segmentation process compared with the classical region competition model our approach gives soft segmentation results via the fuzzy membership functions and moreover the use of frequency data provides additional region information that can improve the overall segmentation result to efficiently solve the minimization of the energy functional we adopt an alternate minimization procedure and make use of chambolle s fast duality projection algorithm we apply the proposed method to synthetic and natural textures as well as real world natural images experimental results show that our proposed method has very promising segmentation performance compared with the current state of the art approaches

statistical texture characterization from discrete wavelet representations we conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets 1 the wavelet histogram signatures which capture all first order statistics using a model based approach and 2 the wavelet co occurrence signatures which reflect the coefficients second order statistics the introduced feature sets outperform the traditionally used energy best performance is achieved by combining histogram and co occurrence signatures

dual models of interval dea and its extension to interval data abstract the purpose of this paper is to develop a new dea with an interval efficiency an original dea model is to evaluate each dmu optimistically there is another model called inverted dea to evaluate each dmu pessimistically but there are no relations essentially between dea and inverted dea thus we formulate a dea model with an interval efficiency which consists of efficiencies obtained from the optimistic and pessimistic viewpoints thus two end points can construct an interval efficiency with the same idea we deal with the interval inefficiency model which is inverse to interval efficiency finally we extend the proposed dea model to interval data and fuzzy data

transform and hybrid transform dpcm coding of images using pyramid vector quantization the transform and hybrid transform dpcm methods of image coding are generalized to allow pyramid vector quantization of the transform coefficients an asymptotic mean squared error performance expression is derived for the pyramid vector quantizer and used to determine the optimum rate assignment for encoding the various transform coefficients coding simulations for two images at average rates of 0 5 1 bit pixel demonstrate a 1 3 db improvement in signal to noise ratio for the vector quantization approach in the hybrid coding with more modest improvements in signal to noise ratio in the transform coding however this improvement is quite noticeable in image quality particularly in reducing blockiness in the low bit rate encoded images

estimation from quantized gaussian measurements when and how to use dither subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals however estimation from dithered measurements often naively applies the sample mean or midrange even when the total noise is not well described with a gaussian or uniform distribution we show that the generalized gaussian distribution approximately describes subtractively dithered quantized samples of a gaussian signal furthermore a generalized gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers the order statistics based estimators outperform both the sample mean and midrange for nontrivial sums of gaussian and uniform noise additional analysis of the generalized gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements specifically we find subtractive dither to be beneficial when the ratio between the gaussian standard deviation and quantization interval length is roughly less than one third when that ratio is also greater than 0 822 k text 0 930 for the number of measurements k text 20 estimators we present are more efficient than the midrange

locally optimum nonlinearities for dct watermark detection the issue of copyright protection of digital multimedia data has attracted a lot of attention during the last decade an efficient copyright protection method that has been gaining popularity is watermarking i e the embedding of a signature in a digital document that can be detected only by its rightful owner watermarks are usually blindly detected using correlating structures which would be optimal in the case of gaussian data however in the case of dct domain image watermarking the data is more heavy tailed and the correlator is clearly suboptimal nonlinear receivers have been shown to be particularly well suited for the detection of weak signals in heavy tailed noise as they are locally optimal this motivates the use of the gaussian tailed zero memory nonlinearity as well as the locally optimal cauchy nonlinearity for the detection of watermarks in dct transformed images we analyze the performance of these schemes theoretically and compare it to that of the traditionally used gaussian correlator but also to the recently proposed generalized gaussian detector which outperforms the correlator the theoretical analysis and the actual performance of these systems is assessed through experiments which verify the theoretical analysis and also justify the use of nonlinear structures for watermark detection the performance of the correlator and the nonlinear detectors in the presence of quantization is also analyzed using results from dither theory and also verified experimentally

image subband coding using context based classification and adaptive quantization adaptive compression methods have been a key component of many proposed subband or wavelet image coding techniques this paper deals with a particular type of adaptive subband image coding where we focus on the image coder s ability to adjust itself on the fly to the spatially varying statistical nature of image contents this backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters specifically we present backward adaptive quantization using a new context based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients we couple this classification with online parametric adaptation of the quantizer applied to each class a simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate distortion performance in particular at very low rates for popular test images it is comparable or superior to most of the state of the art coders in the literature

uniform and piecewise uniform lattice vector quantization for memoryless gaussian and laplacian sources lattice vector quantizer design procedures for nonuniform sources are presented the procedures yield lattice vector quantizers with excellent performance and retaining the structure required for fast quantization analytical methods for truncating and scaling lattices to be used in vector quantizations are given and their utility is demonstrated for independent and identically distributed i i d gaussian and laplacian sources an analytical technique for piecewise linear multidimensional compandor designs is evaluated for i i d gaussian and laplacian sources by comparing its performance to that of the other vector quantizers

a general effective rate control system based on matching measurement and inter quantizer current rate control systems use the frame complexity to determine the frame level bitrates these methods ignore the dependence between the current frame and its reference frames degrade the accuracy of their rate distortion models and reduce the rate control performance as a consequence in this paper we propose a general effective rate control system based on matching measurement and inter quantizer rcmmail for actual video which includes two phrases 1 a novel frame level rate control algorithm based on matching measurement frcmm is proposed to compute the frame level target bitrates which measures the frame matching score to characterize the dependency between the current frame and a reference frame with this quantified dependency a more accurate rate distortion model is obtained 2 based on the frame level target bitrates a novel inter quantizer is proposed to compute the corresponding quantization parameters for each frame this quantizer can solve the problem of the uncertain parameters in the current model improving the accuracy and reducing computational complexity simultaneously experimental results show that the proposed rate control system achieved better peak signal to noise ratio psnr than other compared models meanwhile the average relative rate control error and time consumption is reduced

fuzzy efficiency measures in data envelopment analysis abstract the existing data envelopment analysis dea models for measuring the relative efficiencies of a set of decision making units dmus using various inputs to produce various outputs are limited to crisp data to deal with imprecise data the notion of fuzziness has been introduced this paper develops a procedure to measure the efficiencies of dmus with fuzzy observations the basic idea is to transform a fuzzy dea model to a family of conventional crisp dea models by applying the cut approach a pair of parametric programs is formulated to describe that family of crisp dea models via which the membership functions of the efficiency measures are derived since the efficiency measures are expressed by membership functions rather than by crisp values more information is provided for management by extending to fuzzy environment the dea approach is made more powerful for applications

wavelet modeling using finite mixtures of generalized gaussian distributions application to texture discrimination and retrieval this paper addresses statistical based texture modeling using wavelets we propose a new approach to represent the marginal distribution of the wavelet coefficients using finite mixtures of generalized gaussian mogg distributions the mogg captures a wide range of histogram shapes which provides better description and discrimination of texture than using single probability density functions pdf s as proposed by recent state of the art approaches moreover we propose a model similarity measure based on kullback leibler divergence kld approximation using monte carlo sampling methods through experiments on two popular texture data sets we show that our approach yields significant performance improvements for texture discrimination and retrieval as compared with recent methods of statistical based wavelet modeling

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed