the value of the stochastic solution in stochastic linear programs with fixed recourse stochastic linear programs have been rarely used in practical situations largely because of their complexity in evaluating these problems without finding the exact solution a common method has been to find bounds on the expected value of perfect information in this paper we consider a different method we present bounds on the value of the stochastic solution that is the potential benefit from solving the stochastic program over solving a deterministic program in which expected values have replaced random parameters these bounds are calculated by solving smaller programs related to the stochastic recourse problem

multiple description trellis coded quantization we present a construction of multiple description trellis coded quantizers we use the tensor product of trellises to build a trellis which is applicable to multiple description coding the problems of index assignment and set partitioning for the resulting trellis are considered the viterbi algorithm provides the best path for encoding and the design procedure utilizes a generalized lloyd algorithm the encoding process simultaneously generates all the transmitted sequences furthermore the complexity of the scheme is almost independent of the rate the quantizer provides remarkable performance with little encoding complexity

a hierarchy of bounds for stochastic mixed integer programs we consider general two stage smips with recourse in which integer variables are allowed in both stages of the problem and randomness is allowed in the objective function the constraint matrices i e the technology matrix and the recourse matrix and the right hand side we develop a hierarchy of lower and upper bounds for the optimal objective value of an smip by generalizing the wait and see solution and the expected result of using the expected value solution these bounds become progressively stronger but generally more difficult to compute our numerical study indicates the bounds we develop in this paper can be strong relative to those provided by linear relaxations hence this new bounding approach is a complementary tool to the current bounding techniques used in solving smips particularly for large scale and poorly formulated problems

recognizing multiple human activities and tracking full body pose in unconstrained environments visual observations such as camera images are hard to obtain for long term human motion analysis in unconstrained environments in this paper we present a method for human full body pose tracking and activity recognition from measurements of few body worn inertial orientation sensors the sensors make our approach insensitive to illumination and occlusions and permit a person to move freely since the data provided by inertial sensors is sparse noisy and often ambiguous we use a generative prior model of feasible human poses and movements to constrain the tracking problem our model consists of several low dimensional activity specific manifold embeddings that significantly restrict the search space for pose tracking using a particle filter our method continuously explores multiple pose hypotheses in the embedding space an efficient activity switching mechanism governs the distribution of particles across the activity specific manifold embeddings selecting a pose hypothesis that best explains incoming sensor observations simultaneously allows us to classify the activity a person is performing and to estimate the full body pose we also derive an effective measure of predictive confidence that enables detecting anomalous movements experiments on a multi person data set containing several activities show that our method can seamlessly detect activity switches and accurately reconstruct full body poses from the data of only six wearable inertial sensors

manifold constrained coding and sparse representation for human action recognition due to its various applications human action recognition has been widely studied and achieved tremendous progress however how to learn an accurate and discriminative behavior representation based on the extracted features remains as a challenging problem in this paper we present an effective coding scheme that can discover the manifold structure of the learned features with an l 2 norm regularization coupled with a local constraint the proposed coding scheme which has an analytical solution can learn an accurate compact and yet discriminative behavior representation after the behavior representations are obtained the action recognition problem is formulated as a sparse linear representation of an overcomplete dictionary constructed by labeled behavior representations the same manifold l 2 norm regularization is also employed in this stage the reconstruction error associated with each class is used for classification experimental results demonstrate the effectiveness of the proposed approach on several public datasets including various physical actions and facial expressions

unsupervised learning of human action categories using spatial temporal words we present a novel unsupervised learning method for human action categories a video sequence is represented as a collection of spatial temporal words by extracting space time interest points the algorithm automatically learns the probability distributions of the spatial temporal words and the intermediate topics corresponding to human action categories this is achieved by using latent topic models such as the probabilistic latent semantic analysis plsa model and latent dirichlet allocation lda our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models given a novel video sequence the algorithm can categorize and localize the human action s contained in the video we test our algorithm on three challenging datasets the kth human motion dataset the weizmann human action dataset and a recent dataset of figure skating actions our results reflect the promise of such a simple approach in addition our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions

boosted multi class semi supervised learning for human action recognition human action recognition is a challenging task due to significant intra class variations occlusion and background clutter most of the existing work use the action models based on statistic learning algorithms for classification to achieve good performance on recognition a large amount of the labeled samples are therefore required to train the sophisticated action models however collecting labeled samples is labor intensive to tackle this problem we propose a boosted multi class semi supervised learning algorithm in which the co em algorithm is adopted to leverage the information from unlabeled data three key issues are addressed in this paper firstly we formulate the action recognition in a multi class semi supervised learning problem to deal with the insufficient labeled data and high computational expense secondly boosted co em is employed for the semi supervised model construction to overcome the high dimensional feature space weighted multiple discriminant analysis wmda is used to project the features into low dimensional subspaces in which the gaussian mixture models gmm are trained and boosting scheme is used to integrate the subspace models thirdly we present the upper bound of the training error in multi class framework which is able to guide the novel classifier construction in theory the proposed solution is proved to minimize this upper error bound experimental results have shown good performance on public datasets

embedded image coding using zerotrees of wavelet coefficients the embedded zerotree wavelet algorithm ezw is a simple yet remarkably effective image compression algorithm having the property that the bits in the bit stream are generated in order of importance yielding a fully embedded code the embedded code represents a sequence of binary decisions that distinguish an image from the null image using an embedded coding algorithm an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly also given a bit stream the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream in addition to producing a fully embedded bit stream the ezw consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images yet this performance is achieved with a technique that requires absolutely no training no pre stored tables or codebooks and requires no prior knowledge of the image source the ezw algorithm is based on four key concepts 1 a discrete wavelet transform or hierarchical subband decomposition 2 prediction of the absence of significant information across scales by exploiting the self similarity inherent in images 3 entropy coded successive approximation quantization and 4 universal lossless data compression which is achieved via adaptive arithmetic coding

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

laplacian group sparse modeling of human actions recently many local feature based methods have been proposed for feature learning to obtain a better high level representation of human behavior most of the previous research ignores the structural information existing among local features in the same video sequences while it is an important clue to distinguish ambiguous actions to address this issue we propose a laplacian group sparse coding for human behavior representation unlike traditional methods such as sparse coding our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video level sparsity is guaranteed by incorporating laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved thus a compact but discriminative human behavior representation is achieved besides the objective of our model is solved with a closed form solution which reduces the computational cost significantly promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach

bounds on risk averse mixed integer multi stage stochastic programming problems with mean cvar abstract risk averse mixed integer multi stage stochastic programming forms a class of extremely challenging problems since the problem size grows exponentially with the number of stages the problem is non convex due to integrality restrictions and the objective function is nonlinear in general we propose a scenario tree decomposition approach namely group subproblem approach to obtain bounds for such problems with an objective of dynamic mean conditional value at risk mean cvar our approach does not require any special problem structure such as convexity and linearity therefore it can be applied to a wide range of problems we obtain lower bounds by using different convolution of mean cvar risk measures and different scenario partition strategies the upper bounds are obtained through the use of optimal solutions of group subproblems using these lower and upper bounds we propose a solution algorithm for risk averse mixed integer multi stage stochastic problems with mean cvar risk measures we test the performance of the proposed algorithm on a multi stage stochastic lot sizing problem and compare different choices of lower bounds and partition strategies comparison of the proposed algorithm to a commercial solver revealed that on the average the proposed algorithm yields 1 13 stronger bounds the commercial solver requires additional running time more than a factor of five on the average to reach the same optimality gap obtained by the proposed algorithm

a framework for evaluating the data hiding capacity of image sources an information theoretic model for image watermarking and data hiding is presented in this paper previous theoretical results are used to characterize the fundamental capacity limits of image watermarking and data hiding systems capacity is determined by the statistical model used for the host image by the distortion constraints on the data hider and the attacker and by the information available to the data hider to the attacker and to the decoder we consider autoregressive block dct and wavelet statistical models for images and compute data hiding capacity for compressed and uncompressed host image sources closed form expressions are obtained under sparse model approximations models for geometric attacks and distortion measures that are invariant to such attacks are considered

an adaptive partition based approach for solving two stage stochastic programs with fixed recourse we study an adaptive partition based approach for solving two stage stochastic programs with fixed recourse a partition based formulation is a relaxation of the original stochastic program and we study a finitely converging algorithm in which the partition is adaptively adjusted until it yields an optimal solution a solution guided refinement strategy is developed to refine the partition by exploiting the relaxation solution obtained from a partition in addition to refinement we show that in the case of stochastic linear programs it is possible to merge some components in a partition without weakening the corresponding relaxation bound thus allowing the partition size to be kept small we also show that for stochastic linear programs with simple recourse there exists a small partition that yields an optimal solution the size of this partition is independent of the number of scenarios used in the model our computational results show that the proposed adaptive partition based approach converges v

image coding using wavelet transform a scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed this method involves two steps first a wavelet transform used in order to obtain a set of biorthogonal subclasses of images the original image is decomposed at different scales using a pyramidal algorithm architecture the decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image second according to shannon s rate distortion theory the wavelet coefficients are vector quantized using a multiresolution codebook to encode the wavelet coefficients a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed in order to allow the receiver to recognize a picture as quickly as possible at minimum cost a progressive transmission scheme is presented it is shown that the wavelet transform is particularly well adapted to progressive transmission

channel coding with multilevel phase signals a coding technique is described which improves error performance of synchronous data links without sacrificing data rate or requiring more bandwidth this is achieved by channel coding with expanded sets of multilevel phase signals in a manner which increases free euclidean distance soft maximum likelihood ml decoding using the viterbi algorithm is assumed following a discussion of channel capacity simple hand designed trellis codes are presented for 8 phase shift keying psk and 16 quadrature amplitude shift keying qask modulation these simple codes achieve coding gains in the order of 3 4 db it is then shown that the codes can be interpreted as binary convolutional codes with a mapping of coded bits into channel signals which we call mapping by set partitioning based on a new distance measure between binary code sequences which efficiently lower bounds the euclidean distance between the corresponding channel signal sequences a search procedure for more powerful codes is developed codes with coding gains up to 6 db are obtained for a variety of multilevel phase modulation schemes simulation results are presented and an example of carrier phase tracking is discussed

on the importance of combining wavelet based nonlinear approximation with coding strategies this paper provides a mathematical analysis of transform compression in its relationship to linear and nonlinear approximation theory contrasting linear and nonlinear approximation spaces we show that there are interesting classes of functions random processes which are much more compactly represented by wavelet based nonlinear approximation these classes include locally smooth signals that have singularities and provide a model for many signals encountered in practice in particular for images however we also show that nonlinear approximation results do not always translate to efficient compress on strategies in a rate distortion sense based on this observation we construct compression techniques and formulate the family of functions stochastic processes for which they provide efficient descriptions in a rate distortion sense we show that this family invariably leads to besov spaces yielding a natural relationship among besov smoothness linear nonlinear approximation order and compression performance in a rate distortion sense the designed compression techniques show similarities to modern high performance transform codecs allowing us to establish relevant rate distortion estimates and identify performance limits

space frequency quantization for wavelet image coding a new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree structured quantization related to spatial structures has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation this paper addresses the problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder we consider zerotree quantization zeroing out tree structured sets of wavelet coefficients and the simplest form of scalar quantization a single common uniform scalar quantizer applied to all nonzeroed coefficients and we formalize the problem of optimizing their joint application we develop an image coding algorithm for solving the resulting optimization problem despite the basic form of the two quantizers considered the resulting algorithm demonstrates coding performance that is competitive often outperforming the very best coding algorithms in the literature

aggregation bounds in stochastic linear programming stochastic linear programs become extremely large and complex as additional uncertainties and possible future outcomes are included in their formulation row and column aggregation can significantly reduce this complexity but the solutions of the aggregated problem only provide an approximation of the true solution in this paper error bounds on the value of the optimal solution of the original problem are obtained from the solution of the aggregated problem these bounds apply for aggregation of both random variables and time periods

multiple description wavelet based image coding we consider the problem of coding images for transmission over error prone channels the impairments we target are transient channel shutdowns as would occur in a packet network when a packet is lost or in a wireless system during a deep fade when data is delivered it is assumed to be error free but some of the data may never reach the receiver the proposed algorithms are based on a combination of multiple description scalar quantizers with techniques successfully applied to the construction of some of the most efficient subband coders a given image is encoded into multiple independent packets of roughly equal length when packets are lost the quality of the approximation computed at the receiver depends only on the number of packets received but does not depend on exactly which packets are actually received when compared with previously reported results on the performance of robust image coders based on multiple descriptions on standard test images our coders attain similar psnr values using typically about 50 60 of the bit rate required by these other state of the art coders while at the same time providing significantly more freedom in the mechanism for allocation of redundancy among descriptions

entropy constrained trellis coded quantization trellis coded quantization is generalized to allow noiseless coding of the trellis branch reproduction symbols an entropy constrained trellis coded quantization ectcq design algorithm is presented based on the generalized lloyd algorithm for trellis code design and the entropy constrained vector quantization design algorithm ectcq systems are designed for memoryless gaussian and laplacian sources and for first and second order gauss markov sources with an 8 state trellis the mean squared error ectcq encoding performance for memoryless sources with smooth densities is within about 0 5 db of the rate distortion function small additional improvements are achieved with larger trellises similar though not quite as good performance is achieved for the gauss markov sources by using the ectcq in a predictive coding structure

a filter based bit allocation scheme for subband compression of hdtv the authors compare the subband compression capabilities of eight filter sets consisting of linear phase quadrature mirror filters qmfs perfect reconstruction filters and nonlinear phase wavelets at different bit rates using a filter based bit allocation procedure using dpcm and pcm in hdtv subband coding it is found that qmfs have an edge over the rest

image coding using wavelet transforms and entropy constrained trellis coded quantization the discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multi resolution approximations multi resolution decomposition schemes have proven to be very effective for high quality low bit rate image coding in this work we investigate the use of entropy constrained trellis coded quantization ectcq for encoding the wavelet coefficients of both monochrome and color images ectcq is known as an effective scheme for quantizing memoryless sources with low to moderate complexity the ectcq approach to data compression has led to some of the most effective source codes found to date for memoryless sources performance comparisons are made using the classical quadrature mirror filter bank of johnston and nine tap spline filters that were built from biorthogonal wavelet bases we conclude that the encoded images obtained from the system employing nine tap spline filters are marginally superior although at the expense of additional computational burden excellent peak signal to noise ratios are obtained for encoding monochrome and color versions of the 512 spl times 512 lenna image comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive

fast variable run length coding for embedded progressive wavelet based image compression run length coding has attracted much attention in wavelet based image compression because of its simplicity and potentially low complexity the main drawback is the inferior rd performance compared to the state of the art coder spiht in this paper we concentrate on the embedded progressive run length code of tian and wells 1996 1998 we consider significance sequences drawn from the scan in the dominant pass it turns out that self similar curves for scanning the dominant pass increase the compression efficiency significantly this is a consequence of the correlation of direct neighbors in the wavelet domain this dependence can be better exploited by using groups of coefficients similar to the spiht algorithm this results in a new and very fast coding algorithm which shows performance similar to the state of the art coder spiht but with lower complexity and small and fixed memory overhead

subband finite state scalar quantization a new image coding algorithm that exploits the relationship between various subbands of an image is presented in this correspondence using local variance for state selection and uniform threshold quantizers on subbands we have achieved a reduction in quantization noise compared to other published techniques for lena at various bit rates

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

latent semantic learning with structured sparse representation for human action recognition this paper proposes a novel latent semantic learning method for extracting high level latent semantics from a large vocabulary of abundant mid level features i e visual keywords with structured sparse representation which can help to bridge the semantic gap in the challenging task of human action recognition to discover the manifold structure of mid level features we develop a graph based spectral embedding approach to latent semantic learning with the graph over mid level features being constructed using sparse representation moreover we define new l 1 norm hypergraph regularization to induce extra structured sparsity into sparse representation for graph construction due to the nice properties sparsity and noise robustness of such structured sparse representation our graph construction can capture dominant and robust relationships among mid level features which are crucial for the success of latent semantic learning in action recognition unlike the traditional latent semantic analysis based on topic models our latent semantic learning method can explore the manifold structure of mid level features in both graph construction and spectral embedding which results in compact but discriminative high level features the experimental results on the commonly used kth action dataset and unconstrained youtube action dataset show the promising performance of our method

mutual information based analysis of jpeg2000 contexts context based arithmetic coding has been widely adopted in image and video compression and is a key component of the new jpeg2000 image compression standard in this paper the contexts used in jpeg2000 are analyzed using the mutual information which is closely related to the compression performance we first show that when combining the contexts the mutual information between the contexts and the encoded data will decrease unless the conditional probability distributions of the combined contexts are the same given i the initial number of contexts and f the final desired number of contexts there are s i f possible context classification schemes where s i f is called the stirling number of the second kind the optimal classification scheme is the one that gives the maximum mutual information instead of using an exhaustive search the optimal classification scheme can be obtained through a modified generalized lloyd algorithm with the relative entropy as the distortion metric for binary arithmetic coding the search complexity can be reduced by using dynamic programming our experimental results show that the jpeg2000 contexts capture the correlations among the wavelet coefficients very well at the same time the number of contexts used as part of the standard can be reduced without loss in the coding performance

comparison of different methods of classification in subband coding of images this paper investigates various classification techniques applied to subband coding of images as a way of exploiting the nonstationary nature of image subbands the advantages of subband classification are characterized in a rate distortion framework in terms of classification gain and overall subband classification gain two algorithms maximum classification gain and equal mean normalized standard deviation classification which allow unequal number of blocks in each class are presented the dependence between the classification maps from different subbands is exploited either directly while encoding the classification maps or indirectly by constraining the classification maps the trade off between the classification gain and the amount of side information is explored coding results for a subband image coder based on classification are presented the simulation results demonstrate the value of classification in subband coding

accurate 3d action recognition using learning on the grassmann manifold in this paper we address the problem of modeling and analyzing human motion by focusing on 3d body skeletons particularly our intent is to represent skeletal motion in a geometric and efficient way leading to an accurate action recognition system here an action is represented by a dynamical system whose observability matrix is characterized as an element of a grassmann manifold to formulate our learning algorithm we propose two distinct ideas 1 in the first one we perform classification using a truncated wrapped gaussian model one for each class in its own tangent space 2 in the second one we propose a novel learning algorithm that uses a vector representation formed by concatenating local coordinates in tangent spaces associated with different classes and training a linear svm we evaluate our approaches on three public 3d action datasets msr action 3d ut kinect and ucf kinect datasets these datasets represent different kinds of challenges and together help provide an exhaustive evaluation the results show that our approaches either match or exceed state of the art performance reaching 91 21 on msr action 3d 97 91 on ucf kinect and 88 5 on ut kinect finally we evaluate the latency i e the ability to recognize an action before its termination of our approach and demonstrate improvements relative to other published approaches highlightsa human action recognition approach which represents skeletal sequence as point on the grassmann manifold a new learning algorithm is introduced for learning human actions experiments are performed on three public datasets promising success rates are achieved showing accuracy and better latency performances

significance linked connected component analysis for wavelet image coding the success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation there have been several very competitive wavelet coders developed namely shapiro s 1993 embedded zerotree wavelets ezw servetto et al s 1995 morphological representation of wavelet data mrwd and said and pearlman s see ieee trans circuits syst video technol vol 6 p 245 50 1996 set partitioning in hierarchical trees spiht we develop a novel wavelet image coder called significance linked connected component analysis slcca of wavelet coefficients that extends mrwd by exploiting both within subband clustering of significant coefficients and cross subband dependency in significant fields extensive computer experiments on both natural and texture images show convincingly that the proposed slcca outperforms ezw mrwd and spiht for example for the barbara image at 0 25 b pixel slcca outperforms ezw mrwd and spiht by 1 41 db 0 32 db and 0 60 db in psnr respectively it is also observed that slcca works extremely well for images with a large portion of texture for eight typical 256 spl times 256 grayscale texture images compressed at 0 40 b pixel slcca outperforms spiht by 0 16 db 0 63 db in psnr this performance is achieved without using any optimal bit allocation procedure thus both the encoding and decoding procedures are fast

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

universal trellis coded quantization a new form of trellis coded quantization based on uniform quantization thresholds and on the fly quantizer training is presented the universal trellis coded quantization utcq technique requires neither stored codebooks nor a computationally intense codebook design algorithm its performance is comparable with that of fully optimized entropy constrained trellis coded quantization ectcq for most encoding rates the codebook and trellis geometry of utcq are symmetric with respect to the trellis superset this allows sources with a symmetric probability density to be encoded with a single variable rate code rate allocation and quantizer modeling procedures are given for utcq which allow access to continuous quantization rates an image coding application based on adaptive wavelet coefficient subblock classification arithmetic coding and utcq is presented the excellent performance of this coder demonstrates the efficacy of utcq we also present a simple scheme to improve the perceptual performance of utcq for certain imagery at low bit rates this scheme has the added advantage of being applied during image decoding without the need to reencode the original image

fast reconstruction of subband decomposed progressively transmitted signals we propose a fast reconstruction method for a subband decomposed progressive signal coding system we show that unlike the conventional approach which requires a fixed computational complexity the computational complexity of the proposed approach is proportional to the number of refined coefficients at each level of progression therefore unrefined coefficients do not add to the computational complexity of the proposed scheme it is shown through specific examples that the proposed approach can lead to significant reductions in reconstruction complexity furthermore the proposed approach provides the capability for an online updating of the reconstructed image based on receiving the refinement of each coefficient

content based multiple bitstream image transmission over noisy channels we propose a novel combined source and channel coding scheme for image transmission over noisy channels the main feature of the proposed scheme is a systematic decomposition of image sources so that unequal error protection can be applied according to not only bit error sensitivity but also visual content importance the wavelet transform is adopted to hierarchically decompose the image the association between the wavelet coefficients and what they represent spatially in the original image is fully exploited so that wavelet blocks are classified based on their corresponding image content the classification produces wavelet blocks in each class with similar content and statistics therefore enables high performance source compression using the set partitioning in hierarchical trees spiht algorithm to combat the channel noise an unequal error protection strategy with rate compatible punctured convolutional cyclic redundancy check rcpc crc codes is implemented based on the bit contribution to both peak signal to noise ratio psnr and visual quality at the receiving end a postprocessing method making use of the spiht decoding structure and the classification map is developed to restore the degradation due to the residual error after channel decoding experimental results show that the proposed scheme is indeed able to provide protection both for the bits that are more sensitive to errors and for the more important visual content under a noisy transmission environment in particular the reconstructed images illustrate consistently better visual quality than using the single bitstream based schemes

solving slp recourse problems with arbitrary multivariate distributions the dependent case bounding the expectation of a convex function of a multivariate random variable is of great importance for solving stochastic linear programming problems slp with recourse the classic bounds are those of jensen and edmundson madansky whereby jensen s inequality is available in the independent as well as in the dependent case in contrast with the edmundson madansky e m inequality madansky a 1959 bounds on the expectation of a convex function of multivariate random variable ann math statist 30 743 746 which is valid only in the independent case this paper presents a straightforward extension of the e m inequality for the dependent case yielding a discrete distribution that is extremal with respect to a partial ordering of a set of distribution functions further we prove that the bounds for the expectation behave monotonically by applying the obtained inequality to smaller and smaller subintervals as this requires the evaluation of conditional probabilities and expectations we replace the given distribution by a discrete one resulting from sampling and determine the sampling size that keeps the statistical error negligible finally we conclude with the application to slp recourse problems and state some computational results illustrating the effort for solving recourse problems

progressive image coding using trellis coded quantization in this work we present coding techniques that enable progressive transmission when trellis coded quantization tcq is applied to wavelet coefficients a method for approximately inverting tcq in the absence of least significant bits is developed results are presented using different rate allocation strategies and different entropy coders the proposed wavelet tcq coder yields excellent coding efficiency while supporting progressive modes analogous to those available in jpeg

fusing appearance and distribution information of interest points for action recognition most of the existing action recognition methods represent actions as bags of space time interest points specifically space time interest points are detected from the video and described using appearance based descriptors each descriptor is then classified as a video word and a histogram of these video words is used for recognition these methods therefore rely solely on the discriminative power of individual local space time descriptors whilst ignoring the potentially useful information about the global spatio temporal distribution of interest points in this paper we propose a novel action representation method which differs significantly from the existing interest point based representation in that only the global distribution information of interest points is exploited in particular holistic features from clouds of interest points accumulated over multiple temporal scales are extracted since the proposed spatio temporal distribution representation contains different but complementary information to the conventional bag of words representation we formulate a feature fusion method based on multiple kernel learning experiments using the kth and weizmann datasets demonstrate that our approach outperforms most existing methods in particular under occlusion and changes in view angle clothing and carrying condition

trellis coded quantization of memoryless and gauss markov sources trellis coded quantization tcq is developed and applied to the encoding of memoryless and gauss markov sources the theoretical justification for the approach is alphabet constrained rate distortion theory which is a dual to the channel capacity argument that motivates trellis coded modulation tcm the authors adopt the notions of signal set expansion set partitioning and branch labeling of tcm but modify the techniques to account for the source distribution to design tcq coders of low complexity with excellent mean squared error mse performance for a memoryless uniform source tcq provides an mse within 0 21 db of the distortion rate bound at all positive integral rates the performance is superior to that promised by the coefficient of quantization for all of the best lattices known in dimensions 24 or less for a memoryless gaussian source the tcq performance at rates of 0 5 1 and 2 b sample is superior to all previous results the authors found in the literature the encoding complexity of tcq is very modest tcq is incorporated into a predictive coding structure for the encoding of gauss markov sources simulation results for first second and third order gauss markov sources are presented

tiling and adaptive image compression we investigate the task of compressing an image by using different probability models for compressing different regions of the image in this task using a larger number of regions would result in better compression but would also require more bits for describing the regions and the probability models used in the regions we discuss using quadtree methods for performing the compression we introduce a class of probability models for images the k rectangular tilings of an image that is formed by partitioning the image into k rectangular regions and generating the coefficients within each region by using a probability model selected from a finite class of n probability models for an image of size n spl times n we give a sequential probability assignment algorithm that codes the image with a code length which is within o k log nn k of the code length produced by the best probability model in the class the algorithm has a computational complexity of o nn sup 3 an interesting subclass of the class of k rectangular tilings is the class of tilings using rectangles whose widths are powers of two this class is far more flexible than quadtrees and yet has a sequential probability assignment algorithm that produces a code length that is within o k log nn k of the best model in the class with a computational complexity of o nn sup 2 logn similar to the computational complexity of sequential probability assignment using quadtrees we also consider progressive transmission of the coefficients of the image

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

image compression via joint statistical characterization in the wavelet domain we develop a probability model for natural images based on empirical observation of their statistics in the wavelet transform domain pairs of wavelet coefficients corresponding to basis functions at adjacent spatial locations orientations and scales are found to be non gaussian in both their marginal and joint statistical properties specifically their marginals are heavy tailed and although they are typically decorrelated their magnitudes are highly correlated we propose a markov model that explains these dependencies using a linear predictor for magnitude coupled with both multiplicative and additive uncertainties and show that it accounts for the statistics of a wide variety of images including photographic images graphical images and medical images in order to directly demonstrate the power of the model we construct an image coder called epwic embedded predictive wavelet image coder in which subband coefficients are encoded one bitplane at a time using a nonadaptive arithmetic encoder that utilizes conditional probabilities calculated from the model bitplanes are ordered using a greedy algorithm that considers the mse reduction per encoded bit the decoder uses the statistical model to predict coefficient values based on the bits it has received despite the simplicity of the model the rate distortion performance of the coder is roughly comparable to the best image coders in the literature

adaptive image coding using spectral classification we present a new classification scheme dubbed spectral classification which uses the spectral characteristics of the image blocks to classify them into one of a finite number of classes a vector quantizer with an appropriate distortion measure is designed to perform the classification operation the application of the proposed spectral classification scheme is then demonstrated in the context of adaptive image coding it is shown that the spectral classifier outperforms gain based classifiers while requiring a lower computational complexity

embedded trellis coded quantization for jpeg2000 a modified embedded trellis coded quantization tcq for jpeg2000 is presented in this paper the method for approximately inverting tcq in the absence of the least significant bits is improved experimental results presented using the optimal rate control algorithm and different embedded tcq formulations show that modified embedded tcq yields significant performance improvement compared to the original one in jpeg2000

eigen image based compression for the image based relighting with cascade recursive least squared networks this paper presents a principal component analysis pca based data compression method for the image base relighting ibl technology which needs tremendous reference images to produce high quality rendering the method contains two main steps eigen image based representation and eigen image compression we extract eigen images by the cascade recursive least squared crls networks based pca due to the large data dimension by keeping only a few important eigen images which are enough to describe the ibl data set the data size can be drastically reduced to further reduce the data size we use the embedded zero wavelet ezw approach to compress those retained eigen images and use uniform quantization plus arithmetic coding to compress the representing coefficients simulation results demonstrate that our approach is superior to that of compressing reference images separately with jpeg or ezw

on the rate distortion efficiency of subband coding it is shown that for the realizable subband coding of a wide sense stationary gaussian source with the implied separate encoding of the subband signals the encoding performance is generally inferior to the rate distortion function of the source it is established that for small encoding distortion except for special cases the subband coding is generally suboptimum

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