The adoption of a Natural Scene Statistics (NSS) model has been an important research direction in the selection of perceptual features capable of giving satisfactory results in the problem of image quality assessment (IQA). In this work, trying to improve the performance of a blind IQA methodology, we simultaneously consider quality aware features from both the spatial and the transform domains. Moreover, for the first time, a statistical description of the spatial domain is investigated through the Student's t distribution, trying to predict the subjective evaluation of humans and to reduce the total number of features. In essence, a large number of features are used, which are optimized by the consequent characterization with the distribution's parameters. The proposed model is then fed to a tool to learn a simple regression model. In this way the extracted trained model is used to predict the graded image quality score, based on known publicly available datasets. The results are interesting and show high levels of agreement with the subjective human perception while maintaining a low total number of features.

In this work, we consider contourlet based image decomposition as a convenient framework for localized representation of images simultaneously in space, frequency (scale) and orientation. Since the subband marginal distributions of natural images in the contourlet domain are highly non-Gaussian with leptokurtotic behavior, we propose the Student's t probability density function (pdf) as a prior for modeling the contourlet coefficients of natural images. As an extension, we also consider the bivariate form of Student's t pdf in order to capture the across scales dependencies of the contourlet transform. We validate our proposal by adopting subjective and objective measures.

In this work, we investigate an asymptotically optimal blind zero-bit watermark detector in the wavelet domain. More specifically, assuming that the marginal distribution of detail coefficients is non-Gaussian, we model it with the Student's t probability density function. Furthermore, we assume that the embedding power of the hidden information is unknown, suggesting in this way a new test statistic based on the Rao hypothesis test. The proposed detector exhibits better performance in terms of detection sensitivity and robust properties compared with other known methods in the framework of non-Gaussian environment. Additionally, we investigate a fixed-parameterization approach towards a lightweight detection with regard of time complexity.

In this work, which addresses issues related to the efficient hiding of watermark information in the transform domain, we propose to model the transform coefficients with the Student-t distribution through the multiplicative rule of embedding. Based on the observation that the statistical distribution of the transform coefficients has heavy tailed behavior, we design a new class of watermark detectors following the multiplicative rule of embedding. We present experimental results that compare the proposed method with known state-of-the-art multiplicative watermark detectors and demonstrate its effectiveness in terms of sensitivity and robustness.

In this paper we propose a new quality metric to estimate the impact of packet loss on the perceptual quality of encoded video sequences transmitted over error-prone networks. The proposed metric, henceforth referred to as Cumulative Distortion using Structural Similarity (CDSSIM), quantifies the overall structural distortion resulting from bidirectional error propagation in predictively coded, motion compensated videos. Furthermore, we present a No-Reference (NR) sparse regression model to predict the proposed CDSSIM metric using pre-defined features associated with slice loss. The Least Absolute Shrinkage and Selection Operator (LASSO) method is applied for two resolution formats with features extracted solely from the encoded bit-stream. Standardized statistical performance measures show that the model can predict the cumulative distortion to a high degree of accuracy. We further evaluate the results using a Quartile-Based Prioritization (QBP) scheme and demonstrate that the predicted data provides an effective way to prioritize packets for video streaming applications.

A method for the efficient encoding and decoding of a shape with application to edge map image compression is proposed. The method relies on the modeling of the manifold of a shape by line segments. Then, encoding is performed by collecting the characteristic features of each line segment, namely the starting and ending points and the number of points contributing to the computation of the corresponding segment. The reconstruction of the shape may be obtained by uniform sampling of points from each line segment computed in the encoding process. Experimental analysis demonstrates that in case of employing a robust and efficient line segment detection algorithm, the proposed encoding/decoding scheme exhibits high compression rates, compared to widely used lossless compression methods, while providing low distortion values.

In order to estimate subjective video quality, we usually deal with a large number of features and a small sample set. Applying regression on complex datasets may lead to imprecise solutions due to possibly irrelevant or noisy features as well as the effect of overfitting. In this work, we propose a No-Reference (NR) method for the estimation of the quality of videos that are impaired by both compression artifacts and packet losses. Particularly, in an effort to establish a robust regression model that generalizes well to unknown data and to increase Mean Opinion Score (MOS) estimation accuracy, we propose a frame-level MOS estimation approach, where the MOS estimate of a sequence is obtained by averaging the per-frame MOS estimates, instead of performing regression directly at the sequence-level. Since it is impractical to obtain the actual per-frame MOS values through subjective experiments, we propose an objective metric able to do this task. Thus, our proposed NR method has the dual benefit of offering improved sequence-level MOS estimation accuracy, while giving an indication of the relative quality of each individual video frame.

Performance guarantees for the algorithms deployed to solve underdetermined linear systems with sparse solutions are based on the assumption that the involved system matrix has the form of an incoherent unit norm tight frame. Learned dictionaries, which are popular in sparse representations, often do not meet the necessary conditions for signal recovery. In compressed sensing (CS), recovery rates have been improved substantially with optimized projections; however, these techniques do not produce binary matrices, which are more suitable for hardware implementation. In this paper, we consider an underdetermined linear system with sparse solutions and propose a preconditioning technique that yields a system matrix having the properties of an incoherent unit norm tight frame. While existing work in preconditioning concerns greedy algorithms, the proposed technique is based on recent theoretical results for standard numerical solvers such as BP and OMP. Our simulations show that the proposed preconditioning improves the recovery rates both in sparse representations and CS; the results for CS are comparable to optimized projections.

This paper investigates quality-driven cross-layer optimization for resource allocation in direct sequence code division multiple access wireless visual sensor networks. We consider a single-hop network topology, where each sensor transmits directly to a centralized control unit (CCU) that manages the available network resources. Our aim is to enable the CCU to jointly allocate the transmission power and source-channel coding rates for each node, under four different quality-driven criteria that take into consideration the varying motion characteristics of each recorded video. For this purpose, we studied two approaches with a different tradeoff of quality and complexity. The first one allocates the resources individually for each sensor, whereas the second clusters them according to the recorded level of motion. In order to address the dynamic nature of the recorded scenery and re-allocate the resources whenever it is dictated by the changes in the amount of motion in the scenery, we propose a mechanism based on the particle swarm optimization algorithm, combined with two restarting schemes that either exploit the previously determined resource allocation or conduct a rough estimation of it. Experimental simulations demonstrate the efficiency of the proposed approaches.

Despite the important properties of unit norm tight frames (UNTFs) and equiangular tight frames (ETFs), their construction has been proven extremely difficult. The few known techniques produce only a small number of such frames while imposing certain restrictions on frame dimensions. Motivated by the application of incoherent tight frames in compressed sensing (CS), we propose a methodology to construct incoherent UNTFs. When frame redundancy is not very high, the achieved maximal column correlation becomes close to the lowest possible bound. The proposed methodology may construct frames of any dimensions. The obtained frames are employed in CS to produce optimized projection matrices. Experimental results show that the proposed optimization technique improves CS signal recovery, increasing the reconstruction accuracy. Considering that the UNTFs and ETFs are important in sparse representations, channel coding, and communications, we expect that the proposed construction will be useful in other applications, besides the CS.

In this paper we consider the problem of resource management for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless Visual Sensor Network (VSN) in a generalized fading environment. In a VSN application, the primary goal is ensuring that maximum video quality is achieved in spite of the prevailing network resource constraints. The Nash Bargaining Solution (NBS) was used in determining the transmission power and source and channel coding rates for each node. The nodes in the network negotiate in order to determine their transmission parameters. The task is to optimize the transmission powers (which are continuous) and the source and channel coding rates (which are discrete) for all the network nodes. Particle Swarm Optimization (PSO) is used to solve the mixed-integer optimization that arises. The analysis was carried out for a myriad of wireless multipath fading environments using a unified moment generating function (MGF) approach.

Multiple Description coding (MD) has been proved to be very efficient for video multicast over random networks when combined with network coding. However, in the case where heterogeneous receivers are present in the network, the impact of the method is bounded by the fact that the solution to the inherent rate allocation problem is based on a compromise between low and high bandwidth receivers. To tackle this problem, we propose a novel video multicast scheme that enhances the combination of MD and network coding with a mechanism that allows all intermediate nodes to actively participate in MD coding by adjusting the size of the video descriptions. In this way, it is possible to tune the rate of the transmitted video stream in order to meet the needs of each receiver. The proposed method significantly improves the quality of the video delivered to all the receivers in the network, regardless of their heterogeneity.

The problem of resource management in a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless Visual Sensor Network (VSN) with M-array Phase Shift Keying (MPSK) modulation in an Additive White Gaussian Network (AWGN) channel was considered in this paper. Achieving maximum video quality, in spite of the prevailing network resource constraints, is of utmost importance in VSN applications. Our optimization scheme is based on the Nash Bargaining Solution (NBS). The nodes in the network negotiate in order to determine their transmission parameters (transmission powers; source and channel coding rates for each node). The task is to optimize the transmission powers (which are continuous) and the source and channel coding rates (which are discrete) for all the network nodes, while taking advantage of the improved bandwidth spectral efficiency provided by the higher order constellation.

Applications that include real-time video delivery are demanding on network performance, while the various network resources are usually constrained. This fact boosts the need for efficient resource management, aiming at the amelioration of the video quality that reaches the end-user. The present paper considers a wireless direct sequence code division multiple access visual sensor network, which employs a cross-layer design. The objective is the maximization of the nodes' utilities under the constraints of a maximum bit rate and a maximum power level for each node. In this vein, the Kalai-Smorodinsky bargaining solution is applied, which is geometrically derived from the graphical representations of the utility sets, under a centralized topology. Ultimately, we have to deal with an optimization problem that concerns the optimal determination of the source coding rates, channel coding rates, and power levels of all nodes of the network, under certain modeling conditions. The experimental results provided by the Kalai-Smorodinsky bargaining solution are compared with results using the Nash bargaining solution and two other schemes that aim at the maximization of an unweighted and a weighted version of the total network utility, respectively. A metric that captures fairness and performance issues is used in order to compare the performance of the schemes. The results are also evaluated in terms of the total consumed power relative with the total achieved utility.

We propose a novel priority-based approach that enables the optimal control of the transmission power and the use of the available network resources of a multihop Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Network (WVSN). TheWVSN nodes can either monitor different scenes (source nodes) or retransmit videos of other nodes (relay nodes). Moreover, in real environments the source nodes monitor different scenes that may be of dissimilar importance. Hence a higher end-to-end quality is demanded for those nodes that are assigned a higher priority. Overall, each node has different power and resource requirements, and therefore a global optimization approach is required. For the purpose of enhancing the delivered video quality of the source nodes with respect to their priorities, we define and suggest the use of priority-based optimization criteria. Experimental results that assess the proposed approach are provided and conclusions are drawn.

In the present paper, we deal with the problem of allocating the network resources in multi-rate Direct Sequence Code Division Multiple Access (DS-CDMA) Visual Sensor Networks (VSNs). We consider a single-cell system where each node uses the same chip rate, but can transmit at a different bit rate. In wireless VSNs, we face the constraints of limited power lifetime and of an error-prone environment, mainly due to attenuation and interference. The proposed cross-layer scheme enables the Centralized Control Unit (CCU) to jointly allocate the transmission power, the transmission bit rate and the source-channel coding rates for each VSN node in order to optimize the delivered video quality. The transmission power of each visual sensor assumes values from a continuous range, while the rest of the resources take values chosen from an available discrete set. The numerical results demonstrate the performance of the proposed multi-rate scheme vs a single-rate system.

In this work, we propose an adaptive M-estimation scheme for robust image super-resolution. The proposed algorithm relies on a maximum a posteriori (MAP) framework and addresses the presence of outliers in the low resolution images. Moreover, apart from the robust estimation of the high resolution image, the contribution of the method is twofold: (i) the robust computation of the regularization parameters controlling the relative strength of the prior with respect to the data fidelity term and (ii) the robust estimation of the optimal step size in the update of the high resolution image. Experimental results demonstrate that integrating these estimations into a robust framework leads to significant improvement in the accuracy of the high resolution image.

In objectbased video representation, video scenes are composed of several arbitrarily shaped video objects (VOs), defined by their texture, shape and motion. In errorprone communications, packet loss results in missing information at the decoder. The impact of transmission errors is minimised through error concealment. In this paper, we propose a spatial error concealment technique for recovering lost shape data. We consider a geometric shape representation consisting of the object boundary, which can be extracted from the -plane. Missing macroblocks result in a broken boundary. A Bspline curve is constructed to replace a missing boundary segment, based on a T spline representation of the received boundary. We use Tsplines because they produce shapepreserving approximations and do not change the characteristics of the original boundary. The representation ensures a good estimation of the first derivatives at the points touching the missing segment. Applying smoothing conditions, we manage to construct a new spline that joins smoothly with the received boundary, leading to successful concealment results. Experimental results on object shapes with different concealment difficulty demonstrate the performance of the proposed method. Comparisons with prior proposed methods are also presented.

We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN). We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and channel coding rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and channel coding rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixed-integer optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.

Accurate image registration plays a preponderant role in image super-resolution methods and in the related literature landmark based registration methods have gained increasing acceptance in this framework. However, their solution relies on point correspondences and on least squares estimation of the registration parameters necessitating further improvement. In this work, a maximum a posteriori scheme for image super-re solution is presented where the image registration part is accomplished in two steps. At first, the low resolution images are registered by establishing correspondences between robust SIFT features. In the second step, the estimation of the registration parameters is fine-tuned along with the estimation of the high resolution image, in an iterative procedure, using the maximization of the mutual information criterion. Numerical results showed that the reconstructed image is consistently of higher quality than in standard MAP-based methods employing only landmarks.

We propose a novel approach for the optimized network resource management of a Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The visual sensors monitor different scenes of varying motion levels, thus different network resources need to be allocated to each sensor. For each recorded scene, our approach considers its individual content-related parameters, in contrast with previous methods that group the sensors according to the amount of motion present in the scene and assign the same transmission parameters to all members of a group. Cross-layer optimization is used across the physical, link and application layers. Based on quality-driven criteria (under the constraint of constant chip rate), we allocate to each node a suitable continuous power level, a discrete source coding rate and a discrete channel coding rate. The resulting problem is solved using the Particle Swarm Optimization algorithm. Experimental results demonstrate the performance and efficiency of each criterion.

In this paper, we propose an approach to manage network resources for a direct sequence code division multiple access (DS-CDMA) visual sensor network where nodes monitor scenes with varying levels of motion. It uses cross-layer optimization across the physical layer, the link layer, and the application layer. Our technique simultaneously assigns a source coding rate, a channel coding rate, and a power level to all nodes in the network based on one of two criteria that maximize the quality of video of the entire network as a whole, subject to a constraint on the total chip rate. One criterion results in the minimal average end-to-end distortion amongst all nodes, while the other criterion minimizes the maximum distortion of the network. Our experimental results demonstrate the effectiveness of the cross-layer optimization.

We propose a game-theory-based cross-layer optimization scheme for wireless Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor networks. The scheme uses the Nash Bargaining Solution (NBS), which assumes that the nodes negotiate, with the help of a centralized control unit, on how to allocate resources. The NBS takes into account the video quality each node could achieve without making an agreement. The cross-layer optimization scheme determines the source coding rate, channel coding rate, and transmission power for each node. We compare the proposed game-theory-based scheme with competing schemes that minimize the average or maximum distortion among the nodes. Experimental results are presented and conclusions are drawn.

In error-prone communications, packet loss results in missing information of shape, motion and texture of a video object (VO). Error concealment refers to the recovery of lost information at the decoder. In this paper, we propose a spatial shape error concealment technique. We consider a geometric representation of the shape of a VO consisting of its boundary, which can be extracted from the received a-plane. Some boundary parts are missing due to errors. We propose a method for modeling the received boundary based on a shape-preserving approximation that uses T-splines. Such an approximation provides a good estimation of the direction of a missing boundary segment, which we use to construct a concealment spline that joins smoothly with the received boundary parts.

In this paper, we propose an optimal strategy for the transmission of scalable video over packet-based multiple-input multiple-output (MIMO) systems. The scalable extension of H.264/AVC that provides a combined temporal, quality and spatial scalability is used. For given channel conditions, we develop a method for the estimation of the distortion of the received video and propose different error concealment schemes. We show the accuracy of our distortion estimation algorithm in comparison with simulated wireless video transmission with packet errors. In the proposed MIMO system, we employ orthogonal space-time block codes (O-STBC) that guarantee independent transmission of different symbols within the block code. In the proposed constrained bandwidth allocation framework, we use the estimated end-to-end decoder distortion to optimally select the application layer parameters, i.e., quantization parameter (QP) and group of pictures (GOP) size, and physical layer parameters, i.e., rate-compatible turbo (RCPT) code rate and symbol constellation. Results show the substantial performance gain by using different symbol constellations across the scalable layers as compared to a fixed constellation.

We propose a low-delay low-complexity end-to-end video transmission system that integrates the latest scalable H.264 codec and the full-rate full-diversity quasi-orthogonal space-time block codes. At the encoder of the scalable H.264 codec, we have developed a low-delay low-complexity method for the estimation of the video distortion at the receiver for given channel conditions. The distortion estimation algorithm is validated using experimental results. In this work, we show clearly the advantage of using the quasi-orthogonal space-time block codes (STBC), which provide a higher rate compared to the orthogonal space-time block codes in a wireless video transmission system. This is in spite of the use of a non-optimal signal constellation structure. The performance results are obtained after addressing the bandwidth allocation problem that employs the scalable decoder distortion estimation algorithm for the optimal selection of the application layer and physical layer parameters.

In this paper, an operationally optimal joint shape and texture coding algorithm is proposed that uses shape-adaptive texture coding as well as texture-aware shape coding. The solution is optimal in the operational rate distortion sense, i.e, given the coding setup, the solution will guarantee the smallest possible distortion for a given rate. The shape is approximated using polygons or higher order curves. We also consider biasing the cost function to favor horizontal and vertical edges for the case of polygon approximation (biased polygon approximation). The texture is encoded using shape-adaptive discrete cosine transform or shape-adaptive discrete wavelet transform of the MPEG-4 video codec. A comparison is drawn between the two techniques. Both a fixed-width and a variable-width tolerance band for shape coding are considered. The variable width of the tolerance band is a function of the texture profile, i.e, the width is inversely proportional to the magnitude of the image gradient. Experimental results are presented and conclusions are drawn.

In this paper, we consider the problem of video transmission over wireless generalized multicarrier code division multiple access (GMC-CDMA) systems. Such systems offer deterministic elimination of multiple access interference. A scalable video source codec is used and a multirate setup is assumed, i.e., each video user is allowed to occupy more than one GMC-CDMA channels. Furthermore, each of these channels can utilize a different number of subcarriers. We propose a cross-layer optimization method to select the source coding rate, channel coding rate, number of subcarriers per GMC-CDMA channel and transmission power per GMC-CDMA channel given a maximum transmission power for each video user and an available chip rate. Universal rate distortion characteristics (URDC) are used to approximate the expected distortion at the receiver. The proposed algorithm is optimal in the operational rate distortion sense, subject to the specific setup used and the approximation caused by the use of the URDC. Experimental results are presented and conclusions are drawn.

We propose an efficient strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec (SVC), which provides combined temporal, quality and spatial scalability. At the transmitter, we estimate the decoded video distortion for given channel conditions taking into account the effects of quantization, packet loss and error concealment. The proposed scalable decoder distortion algorithm offers low delay and low complexity. The performance of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. The bandwidth constrained allocation problem considered here is simplified and solved for one O-STBC symbol at a time. Furthermore, we take the advantage of the hierarchical structure of SVC to attain the optimal solution for each group of pictures (GOP) of the video sequence. We incorporate the estimated decoder distortion to optimally select the application layer parameter, i.e., quantization parameter (QP), and physical layer parameters, i.e., channel coding rate and modulation type for wireless video transmission.

We propose an optimal strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec which provides combined temporal, quality and spatial scalability. At the transmitter, we have developed a method for the estimation of the video distortion at the receiver for given channel conditions. The accuracy of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. Rate-compatible punctured convolutional (RCPC) codes are used for unequal error protection of the scalable layers. In the constrained bandwidth allocation framework considered here, we use the estimated decoder distortion to optimally select the application layer parameter, i.e. quantization parameter (QP), and physical layer parameters, i.e. RCPC and modulation.

Scalability is an important desirable property of video codecs. Wavelet-based motion-compensated temporal filtering provides the most powerful scheme for scalable video coding and provides high-compression efficiency that competes with the current state of art codecs. However, the delay introduced by the temporal filtering schemes is sometimes very high, which makes them unsuitable for many real-time applications. In this paper, ue propose a new temporal filter set to minimize delay in 3-D wavelet-based video coding. The new filter set gives a performance at par with existing longer filters. The length of the filter can vary from two to any number of frames depending on delay requirements. If the frames are processed as separate groups of frames (GOFs), the proposed filter set will not have any boundary effects at the GOF. Experimental results are presented and conclusions are drawn.

In this paper, we consider the rate-distortion optimized resource allocation for video transmission over multi-rate wireless direct-sequence code-division-multiple-access (DS-CDMA) channels. We consider the performance of transmitting scalable video over a multipath Rayleigh fading channel via a combination of multi-code multirate CDMA and variable sequence length multirate CDMA channel system. At the receiver, despreading is done using adaptive space-time auxiliary-vector (AV) filters. We propose a new interference cancelling design that uses just a single AV filter for single-user mutirate despreading. Our experimental results show that the proposed interference cancelling design has excellent performance in scalable video transmission over DS-CDMA systems that use a combination of multicode multirate and variable processing gain multirate CDMA. The proposed design takes advantage of the fact that single user's video data is transmitted using two spreading codes, one for the base layer and one for the enhancement layers, and of the fact that these spreading codes can have different processing gains. The proposed interference cancelling design is compared with two conventional single-user multirate CDMA receiver configurations, however now we use an AV filter rather than a simple matched filter. We also propose a resource allocation algorithm for the optimal determination of source coding rate, channel coding rate and processing gain for each scalable layer, in order to minimize the expected distortion at the receiver.

Generalized multi carrier code division multiple access (GMC-CDMA) is a promising technique for achieving high data rate transmission capacity in interference-limited cellular systems. A key challenge in a multirate GMC-CDMA system for video transmission is the optimal power allocation between the sub-channels that maximizes the received video quality subject to a data rate budget. In this paper, we propose a method for joint source and channel coding with optimal power allocation for scalable video transmission over multirate GMC-CDMA wireless links. A GMC-CDMA system offers deterministic elimination of multiple user interference (MUI). A scalable video source codec is used and a multirate setup is assumed i.e., each video user is allowed to occupy more than one GMC-CDMA channels. Each of the GMC-CDMA channels can utilize a different number of subcarriers. The optimal solution selects the set of source and channel coding rates for the user of interest along with the number of sub-carriers and power allocated to each GMC-CDMA channel that minimizes the end to end distortion subject to the rate budget. Universal rate distortion characteristics (URDC) are used along with the channel characteristic plots to obtain the optimal solution. Experimental results are presented and conclusions are drawn.

In this paper, we propose an approach for cross-layer optimization with power control for a direct sequence-code division multiple access (DS-CDMA) visual sensor network where nodes monitor scenes with varying levels of motion. Our technique simultaneously assigns a source coding rate, a channel coding rate, and a power level to all nodes in the network based on two criteria that maximize the quality of video of the entire network as a whole, subject to a constraint on the total chip rate. One method minimizes the average end-to-end distortion amongst all nodes, while the other method minimizes the maximum distortion of the network. To reduce the computational complexity of the solution, universal rate-distortion characteristics (URDCs) are obtained experimentally to relate bit errors probabilities to the distortion of corrupted video. The URDCs are used in conjunction with channel characteristic plots obtained by using rate-compatible punctured convolutional (RCPC) codes for channel coding.

In 3D based wavelet video coders that use longer filters, the PSNR of the reconstructed frames within a GOF varies significantly. This distortion variation is not directly related to the motion in the video sequence as in the case of hybrid video coding schemes. The paper focuses on the problem of controlling erratic variation of distortion in 3D coders. The wavelet filter properties are explored and scaling coefficients for the wavelet filter is calculated with the objective of smoothening the temporal PSNR. The biorthogonal 5/3 wavelet filter is considered in this paper and experimental results are presented for 2D+t and t+2D wavelet coders.

In this paper, we propose an image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. These inaccurate estimates, along with the additive Gaussian noise in the low-resolution (LR) image sequence, result in different noise level for each frame. In the proposed algorithm, the LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated. A translational motion model is assumed. The convergence property of the proposed algorithm is analyzed in detail. Our experimental results using both real and synthetic data show the effectiveness of the proposed algorithm.

We consider the problem of video transmission over wireless generalized multicarrier code division multiple access (GMC-CDMA) systems. Such systems offer deterministic elimination of multiple access interference (MAI). A scalable video source codec is used and a multirate setup is assumed, i.e., each video user is allowed to occupy more than one GMC-CDMA channel. Furthermore, each of these channels can utilize a different number of subcarriers. We solve an optimization problem to determine the source coding and channel coding rates for the user of interest, as well as the number of subcarriers for each GMC-CDMA channel. Experimental results are presented and conclusions are drawn.

In this paper, an optimal framework is proposed for the joint encoding of the shape and texture information in object based video. The solution is optimal in the operational rate distortion sense, i.e, given the coding setup, the solution guarantees the smallest possible distortion for a given rate. The shape is approximated using polygons or higher order curves. We also consider biasing the cost function to favor horizontal and vertical edges, for the case of polygon approximation (biased polygon approximation). The texture is encoded using shape adaptive discrete cosine transform (SA-DCT) or shape adaptive discrete wavelet transform (SA-DWT) of the MPEG-4 video codec. A comparison is drawn between the two techniques. Both a fixed-width and a variable-width tolerance band for shape coding are considered. The variable width of the tolerance band is a function of the texture profile, i.e, the width is inversely proportional to the magnitude of the image gradient. Experimental results are presented and conclusions are drawn.

In traditional digital image restoration, the blurring process of the optic is assumed known. Many previous research efforts have been trying to reconstruct the degraded image or video sequence with either partially known or totally unknown point spread function (PSF) of the optical lens, which is commonly called the blind deconvolution problem. Many methods have been proposed in the application to image restoration. However, little work has been done in the super-resolution scenario. In this paper, we propose a generalized framework of regularized image/video iterative blind deconvolution / super-resolution (IBD-SR) algorithm, using some information from the more matured blind deconvolution techniques from image restoration. The initial estimates for the image restoration help the iterative image/video super-resolution algorithm converge faster and be stable. Experimental results are presented and conclusions are drawn.

In this work, we propose a new temporal filter set to minimize delay in 3D wavelet based video coding, which gives a performance at par with existing longer filters. Our filter set will not have any boundary effects at the group of frames (GOF). The length of the GOF can vary from five to any number of frames depending on delay requirements. We also propose a novel technique of assigning priorities to temporal subbands at different levels to control distortion fluctuation inside a GOF. Experimental results are presented and conclusions are drawn.

In this work, we propose a novel scheme to minimize drift in scalable wavelet based video coding, which gives a balanced performance between compression efficiency and reconstructed quality with less drift. Our drift control mechanism maintains two frame buffers in the encoder and decoder; one that is based on the base layer and the one that is based on the base plus enhancement layers. Drift control is achieved by switching between these two buffers for motion estimation and compensation and base layer information is used for predicting enhancement layers. Our coder is designed for variable bit rate wireless channels and offers high compression efficiency and sustained video quality.

We propose a novel hybrid scalable/multiple description codec which produces a base layer and two multiple description enhancement layers. The base layer is required for decoding. If one or two of the multiple description enhancement layers are also received, the signal-to-noise ratio of the received video sequence is improved. There is no hierarchy in the multiple description enhancement layers. Thus, in contrast with classic scalable coding (SC), if either enhancement layer is lost, the other enhancement layer is still useful to the decoder. This functionality comes at a minimal expense in compression efficiency compared with SC. The layers are constructed using a rate-distortion optimal partitioning of the discrete cosine transform coefficients. Experimental results are presented and conclusions are drawn.

In this paper, we consider the transmission of video over wireless direct-sequence code-division multiple access (DS-CDMA) channels. A layered (scalable) video source codec is used. The layers may be time-multiplexed and transmitted over a single CDMA channel or each layer can be transmitted over a different CDMA channel. For the latter case, spreading codes of different lengths are allowed for each CDMA channel (multirate CDMA). Thus, a different number of chips per bit can be used for the transmission of each scalable layer. For a given fixed energy value per chip and chip rate, the selection of a spreading code length affects the transmitted energy per bit and bit rate for each scalable layer. An MPEG-4 source encoder is used to provide a two-layer signal-to-noise-ratio scalable bit stream. Each of the two layers is channel-coded using rate-compatible punctured convolutional codes. Then, the data are interleaved, spread, carrier-modulated, and transmitted over the wireless channel. A multipath Rayleigh fading channel model is assumed. At the other end, the signal is collected by an antenna array front. After carrier demodulation, multiple-access-interference suppressing despreading is performed using adaptive space-time auxiliary-vector (AV) filters. The choice of the AV space-time receiver is dictated by realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Our experimental results demonstrate the effectiveness of such a multirate DS-CDMA system for wireless video transmission.

In this paper, we propose a balanced multiple description video coding scheme which is based on the partitioning of the discrete cosine transform (DCT) coefficients. Our scheme splits the single-layered stream generated by a standard coder into two correlated and balanced (virtually identical rates and distortions) substreams. The optimization is in the redundancy-rate-distortion sense using Lagrangian relaxation for optimum allocation of redundancy among the blocks in the frame. A greedy algorithm to meet the equal distortions criterion and an algorithm to optimally achieve equal bit rates for the two descriptions are proposed. Our simulation results substantiate our claim of achieving balanced descriptions at high peak signal-to-noise ratios values for any specified redundancy and any target bit rate. Our design complies with the existing motion-compensated DCT-based standards, i.e., each description can be independently decoded by any standard decoder.

In many imaging systems, the resolution of the detector array of the camera is not sufficiently high for a particular application. Furthermore, the capturing process introduces additive noise and the point spread function of the lens and the effects of the finite size of the photo-detectors further degrade the acquired video frames. The goal of resolution enhancement is to estimate a high-resolution image from a sequence of low-resolution images while also compensating for the degradations. We propose a technique for image resolution enhancement with adaptively weighted low-resolution images (channels) and simultaneous estimation of the regularization parameter. The weight coefficients work as the cross-channel fidelity to each low-resolution image, while the regularization parameter acts as the within-channel balance between data and prior model for each channel. Experimental results are presented and conclusions are drawn.

In this paper, we present a rate-distortion optimal hybrid scalable/ multiple-description video codec. Traditional scalable codecs produce bitstreams which can be partitioned into layers that form a hierarchy. Thus, in order for a particular layer to be useful to the decoder, all hierarchically higher layers also need to be available. Conversely, traditional multiple description codecs produce layers with no hierarchy. Thus, any of the layers can be decoded by itself and produce a video sequence of a certain quality. The more layers are available to the decoder, the better the corresponding video quality. The drawback of multiple description coding is that layers need to be correlated at the expense of compression efficiency. We propose a hybrid scalable/multiple description codec which produces a base layer and two multiple description enhancement layers. The base layer is required for decoding. If one or two of the multiple description enhancement layers are also received, the SNR of the received video sequence is improved. There is no hierarchy in the multiple description enhancement layers. The layers are constructed using a rate-distortion optimal partitioning of the DCT coefficients. Experimental results are presented and conclusions are drawn.

In this paper, we propose a balanced multiple description video coding scheme which is based on the partitioning of the DCT coefficients. Our scheme splits the single-layered stream generated by a standard coder into two correlated and balanced (equal rates and distortions) substreams. The optimization is in the redundancy-rate-distortion sense, using Lagrangian relaxation for optimum allocation of redundancy amongst the blocks in the frame. A greedy algorithm, to meet the equal distortions criterion, and an algorithm to optimally achieve equal bit rates for the two descriptions is proposed. Our simulation results substantiate our claim of achieving balanced descriptions at high PSNR values, for any specified redundancy or any target bit rate for the encoder. Our design complies to the existing standards, i.e. each description can be independently decoded by any standard decoder.

In this paper, we consider the transmission of video over multirate wireless direct-sequence code-division multiple access (DS-CDMA) channels. The performance of transmitting scalable video over a multipath Rayleigh fading channel via a combination of multi-code multirate CDMA and variable sequence length multirate CDMA channel system is considered. At the other end, the signal is collected by an antenna array front and despreading is done using adaptive space-time auxiliary-vector (AV) filters. AV filter configurations suitable for multirate detection are designed and the rate-distortion optimization is carried out for each configuration. The experimental results show a comparison of the performance of such multirate DS-CDMA systems for wireless video transmission.

In this letter, we report results on the relative performance of scalable video transmission via a single-rate or a multirate direct sequence code-division multiple-access channel using minimum total squared correlation spreading codes. Our findings demonstrate the superiority of the multirate system on a wide range of chip rates of practical interest.

A major problem in object-oriented video coding and MPEG-4 is the encoding of object boundaries. Traditionally this problem is treated separately from the texture encoding problem. In this paper, we present a vertex-based shape coding method which is optimal in the operational rate-distortion sense and takes into account the texture information of the video frames. This is accomplished by utilizing a variable-width tolerance band whose width is a function of the texture profile. As an example, this width is inversely proportional to the magnitude of the image gradient. Thus, in areas where the confidence in the estimation of the boundary is low and/or coding errors in the boundary will not affect the application (e.g., object-oriented coding and MPEG-4) significantly, a larger boundary approximation error is allowed. We present experimental results which demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose two approaches for video sequence resolution enhancement using maximum a posteriori (MAP) estimation. Huber-Markov random fields (HMRF) are used as prior models. These models can better preserve image discontinuities (edges) when compared with Gaussian prior models. The two proposed approaches differ in the selection of image smoothness measure. The first approach employs a measure that is based on a discrete Laplacian kernel, while the second approach uses a finite difference approximation of second order derivatives at each pixel of the high-resolution image estimate. Experimental results are presented and conclusions are drawn.

We study some of the design tradeoffs of video streaming systems in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the quality of the received video sequence. We also consider the cost to the network user for streaming the video sequence. We have formulated this utility maximization problem as a joint constrained optimization problem where we maximize the difference between the utility and the network cost, subject to the constraint that the decoder buffer does not underflow. In this manner, we can find the optimal tradeoff between video quality and network cost. We present a deterministic dynamic programming approach for both the constant bit rate and renegotiated constant bit rate service classes. Experimental results demonstrate the benefits and the performance of the proposed approach.

In this paper, we develop an approach toward joint source-channel coding for motion-compensated DCT-based scalable video coding and transmission. A framework for the optimal selection of the source and channel coding rates over all scalable layers is presented such that the overall distortion is minimized. The algorithm utilizes universal rate distortion characteristics which are obtained experimentally and show the sensitivity of the source encoder and decoder to channel errors. The proposed algorithm allocates the available bit rate between scalable layers and, within each layer, between source and channel coding. We present the results of this rate allocation algorithm for video transmission over a wireless channel using the H.263 Version 2 signal-to-noise ratio (SNR) scalable codec for source coding and rate-compatible punctured convolutional (RCPC) codes for channel coding. We discuss the performance of the algorithm with respect to the channel conditions, coding methodologies, layer rates, and number of layers.

A joint source-channel coding scheme for scalable video is developed in this paper. An SNR scalable video coder is used and unequal error protection (UEP) is allowed for each scalable layer. Our problem is to allocate the available bit rate across scalable layers and, within each layer, between source and channel coding, while minimizing the end-to-end distortion of the received video sequence. The resulting optimization algorithm we propose utilizes universal rate-distortion characteristic plots. These plots show the contribution of each layer to the total distortion as a function of the source rate of the layer and the residual bit error rate (the error rate that remains after the use of channel coding). Models for these plots are proposed in order to reduce the computational complexity of the solution. Experimental results demonstrate the effectiveness of the proposed approach.

We extend our previous work on joint source-channel coding to scalable video transmission over wireless direct-sequence code-division-multiple-access (DS-CDMA) multipath fading channels. A SNR scalable video coder is used and unequal error protection (UEP) is allowed for each scalable layer. At the receiver-end an adaptive antenna array auxiliary-vector (AV) filter is utilized that provides space-time RAKE-type processing and multiple-access interference suppression. The choice of the AV receiver is dictated by realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Our problem is to allocate the available bit rate of the user of interest between source and channel coding and across scalable layers, while minimizing the end-to-end distortion of the received video sequence. The optimization algorithm that we propose utilizes universal rate-distortion characteristic curves that show the contribution of each layer to the total distortion as a function of the source rate of the layer and the residual bit error rate (the error rate after channel coding). These plots can be approximated using appropriate functions to reduce the computational complexity of the solution.

A major problem in object oriented video coding and MPEG-4 is the encoding of object boundaries. Traditionally, and within MPEG-4, the encoding of shape and texture information are separate steps (the extraction of shape is not considered by the standards). We present a vertex-based shape coding method which is optimal in the operational rate-distortion sense and takes into account the texture information of the video frames. This is accomplished by utilizing a variable-width tolerance band which is proportional to the degree of trust in the accuracy of the shape information at that location. Thus, in areas where the confidence in the estimation of the boundary is not high and/or coding errors in the boundary will not affect the application (object oriented coding, MPEG-4, etc.) significantly, a larger boundary approximation error is allowed. We present experimental results which demonstrate the effectiveness of the proposed algorithm.

In this paper, we introduce a new methodology for signal-to-noise ratio (SNR) video scalability based on the partitioning of the DCT coefficients. The DCT coefficients of the displaced frame difference (DFD) for inter-blocks or the intensity for intra-blocks are partitioned into a base layer and one or more enhancement layers, thus, producing an embedded bitstream. Subsets of this bitstream can be transmitted with increasing video quality as measured by the SNR. Given a bit budget for the base and enhancement layers the partitioning of the DCT coefficients is done in a way that is optimal in the operational rate-distortion sense. The optimization is performed using Lagrangian relaxation and dynamic programming (DP). Experimental results are presented and conclusions are drawn.

In this paper, we introduce a new methodology for SNR video scalability which is based on the partitioning of the DCT coefficients. The partitioning is done in a way that is optimal in the rate-distortion sense. The optimization is performed using Lagrangian relaxation and dynamic programming (DP). Experimental results are presented and conclusions are drawn.

We address the problem of the efficient encoding of object boundaries. This problem is becoming increasingly important in applications such as content-based storage and retrieval, studio and television postproduction, and mobile multimedia applications. The MPEG-4 visual standard will allow the transmission of arbitrarily shaped video objects. The techniques developed for shape coding within the MPEG-4 standardization effort are described and compared first. A framework for the representation of shapes using their contours is presented next. Such representations are achieved using curves of various orders, and they are optimal in the rate-distortion sense. Finally, conclusions are drawn.