This paper describes the use of standard LTE communications signals to perform positioning indoors, despite the difficult propagation conditions in such environments that require a number of strong signals and a wide measurement bandwidth. Experiments show that the CRS transmissions embedded in normal LTE downlink signals from multiple network operators' base stations can together be used to perform horizontal localization indoors around a building, with an accuracy of around 15m for full bandwidth LTE signals. A reference unit (LMU) is required. Alternatively and with more reference measurement units, 3D positioning can be performed using the uplink signals transmitted by the target LTE modem, and it is shown that a set of DM-RS transmissions embedded within normal uplink communications traffic to its base station can be combined to also give measurements of signals with a wide effective bandwidth.

Physically unclonable constants (PUC) are circuits used to embed unique secret bit-words in chips. We propose a simple PUC, with a complexity comparable with an SRAM cell. The proposed scheme is studied both theoretically and by means of simulations and it is shown that the proposed PUC is both unbiased and very stable. In particular, its intra-distance is predicted to be from 10 to 100 times smaller than competitor schemes. Simulations allow to conclude that the advantages of the proposed scheme are relevant enough to make it competitive even if the actual performance of a real implementation, not considered in this paper, will turn out to be an order of magnitude worse than predicted.

In this paper we define the new concept of a perfectly reliable encoder (PRE) for erasure channels as an encoder whose performance is optimal for every value of the loss probability. As a first step toward the design of such an encoder, we propose an idealized extreme unequal error protection encoding scheme. We derive models to optimize the encoder performance. We show by experiments that the proposed scheme approaches the theoretical behavior, and quantify the gap with respect to a scheme specifically designed for a given loss probability.

We consider the problem of true random bit generation from source vectors of independent geometric random variables, reduced modulo M for practical implementation. Independent geometric random variables result from measurements of discretized Poisson processes, which are good models for a number of physical sources. We propose a generalization of the classical approach by Elias, compute theoretical bounds, and evaluate the efficiency of the scheme by means of experiments. The proposed technique shows a significant advantage with respect to the classical approach.

Physically Unclonable Functions (PUFs) have been recently proposed as a way to include, in chips, functions that can act as “fingerprints” of the chip, to be used in applications like chip authentication (strong PUF) or private ID generation (weak PUF). Most of the schemes proposed for weak PUF exploit helper data to make the PUF more reliable. The use of an helper, however, has some drawbacks such as complexity and the introduction of a possible attack point. In this manuscript we propose the use of a new type of weak PUF called helper-less PUFs (H-PUF) that does not require the use of helper data. We analyze theoretically the problem of designing an H-PUF and use the result to show how an H-PUF can be built. We also show that the proposed PUF, albeit being of weak type, can also be used for chip authentication.

Most of latest-generation multimedia systems are equipped with increasingly-effective object detection algorithms (e.g., intelligent video surveillance systems, augmented reality applications, sharing platforms for multimedia data, etc.). Unfortunately, images and video are usually available in compressed formats, which makes object detection more difficult because of the additional distortion noise. In this paper we show that it is possible to mitigate this problem by introducing a rate allocation algorithm that preserves important details for object identification algorithms. We propose a saliency map that identifies crucial elements for detectors. Then, we map saliency values to the value of the quantization parameter to be used by the video coder. Experimental results on HEVC coder show that the proposed rate control algorithm improves the accuracy with respect to the standard strategy.

The widespread of augmented reality applications, cognitive video surveillance, autonomous or supportive navigation systems, has increased the importance of accurate object detection algorithms. However, the presence of noise depending on the characteristics of the acquisition device, on lighting intensity and directions, and on weather conditions, could severely degrade the performance of such applications. As a matter of fact, effective ad-hoc denoising strategies are required since traditional noise removal algorithms designed to improve the quality of the image, could even worsen the accuracy of detection. This paper presents a low-cost adaptive filtering strategy that adapts the characteristics of the filter depending on the impact of each image region on the feature sets. This solution permits improving the correct detection percentage of approximately 30%with respect to using noisy images. The approach is generally intended for object detection algorithms based on Histogram-of-Oriented-Gradients (HOG) and can run in real time on a limited complexity hardware.

In this paper, we propose a procedure to recover lost slices in inter-coded H.264/AVC video frames. The procedure recovers missing pixels by exploiting temporal information. In particular, it considers 4 × 4 blocks and performs a full search procedure on an adaptive size window in the reference frames, in order to determine the best block according to a specified metric. One key point of the proposed technique is the use of a modified metric, that takes into account continuity at the block boundaries as well as the smoothness of the motion field. Experimental results show that the proposed scheme compares favorably with existing state-of-the-art methods, both objectively and subjectively.

Scalable video coding (SVC) was recently standardized as an extension of the H.264/AVC standard. A scalable coder allows to combine different layers of spatial, temporal and quality scalability, and is a viable solution for adaptation to user characteristics and network conditions. On the other hand, Multiple description coding (MDC) can provide error resilience and graceful quality degradation for transmission over error-prone channels. In this paper, we propose a video codec that combines the SVC and MDC coding paradigms. In particular, the enhancement data of the SVC coder are transmitted using MDC. The resulting fully compatible coder takes advantage of the efficiency of SVC and of the robustness of MDC techniques. The effectiveness of the proposed solution is confirmed by experiments.

In this paper we propose a chunkless structure for multimedia distribution over peer-to-peer networks. With the proposed approach, the original multimedia packets are partitioned into several reduced packets whose transmission requires a fraction 1/R of the bandwidth required by the original data. The generation of the reduced packets depends on a random seed that can be randomly chosen by each node. The reconstruction of the original packet is possible as soon as R reduced packets are received from the other peers. The proposed scheme has a very small computational complexity, it is transparent to cryptography and it is independent on the used multimedia signal coding scheme. Analytical expressions of the start-up time and the probability of information recovery are obtained.

Distributed video coding (DVC) has recently been proposed for emerging scenarios, where sources of correlated video do not communicate, as in some video-surveillance applications, or to simplify the coder for video equipment with power consumption constraints. In this paper we propose and compare different DVC schemes. In particular, we propose the use of the wavelet transform and analyze the performance of encoders based on turbo-codes and on a novel modulo-reduction procedure. The proposed schemes do not need feedback from the receiver and use statistical models for the estimation of the required bit-rate. Experimental results show that the proposed schemes have good performance when compared with similar asymmetric video compression schemes, and that DVC can be an interesting option in appropriate scenarios.

The robust transmission of video sequences over wireless LANs presents several challenging problems concerning the presence of packet losses, delays, and bandwidth limitations. Their effect on the visual quality of the sequence reconstructed by the end user can be mitigated by adopting a wireless architecture that is able to support different levels of quality-of-service (QoS), like the IEEE 802.11e standard, and by compressing the video sequence to be transmitted using a robust source coder, like a multiple description coding (MDC) scheme. This paper presents a MDC-based video streaming architecture that tries to adaptively optimize the performance of both solutions by assigning the RTP video packets produced by the MDC encoder to the different QoS classes of IEEE 802.11e. Simulation results show that the performance is significantly improved with respect to a non-adaptive solution.

We consider the problem of robust video streaming over networks that support QoS differentiation, such as the 802.11e wireless network infrastructure. We consider the benefits obtained matching the H.264 data partitioning (DP) mode with such a QoS-based interface. We compare this solution with an innovative scheme which combines a multiple description (MD) coding framework with a QoS-based network. Results are reported using both a simple IID channel model and a more realistic wireless network, simulated using the OmNet++ network simulator.

Summary form only given. We propose an innovative scheme for distributed coding of two correlated variables X and Y. After analyzing the proposed scheme in some detail, we compare it with joint coding via Karhunen-Loeve transform followed by uniform quantization and entropy coding. We carried out some experiments with X = Y + N, where X, Y and W are all uniform or all Gaussian, with W independent from Y. Experimental results show that the proposed distributed coding scheme has very close or better performance than the joint coding procedure in the considered cases

In this paper we present a wavelet domain distributed coder for video which allows for scalability and does not require any feedback channel. Efficient distributed coding is obtained by processing the wavelet transform with a suitable "folding" function and compressing the folded coefficients with a wavelet coder. At the receiver side, we use the statistical properties between similar frames to recover the compressed frame. Experimental results show that she proposed scheme has good performance when compared with similar asymmetric video compression schemes.

In this paper we propose and investigate a frequency domain approach to channel estimation, and detection for ultra wide band (UWB) direct sequence code division multiple access (DS-CDMA) systems. The channel estimation and detection approach is single user based, and operates in the frequency domain. In the presence of multiple access interference (MAI) the algorithm is appropriately modified to include the capability of canceling the interference through the exploitation of its frequency domain correlation.

Multiple description coding has been recently proposed for images and video transmission in packet erasure channels because of its good loss recovery capability. In this paper, we consider the problem of bit allocation and quantization optimization for frame based multiple description coding schemes obtained by means of an oversampled filterbank. We generalize the well known solution of orthogonal filterbanks, where the same quantizer should be used in each channel, to the case of oversampled filterbanks with coefficient losses. The resulting design procedure can be used to adaptively adjust the quantizers to the channel conditions. We show that the proposed approach is beneficial by means of experiments with images and video.

Video streaming over packet switched best-effort networks is a challenging topic, due to low latency, scalability and fault tolerance requirements. Many techniques can be used to deal with delay, loss and the time-varying nature of best-effort networks. In this paper we compare two techniques to improve the performance of video streaming, i.e., a multiple description (MD) scheme based on spatial polyphase downsampling, and a single description (SD) scheme where robustness to packet loss is increased using forward error correcting (FEC) codes. We consider both a single channel scenario and a multiple channel (or multi-path) scenario. We span a large set of channel conditions, to consider the high packet loss probabilities common in wireless communication systems. A H.264/AVC video coding standard with advanced error concealment capabilities is used. Experimental results show that MD can be competitive in practical scenarios with more flexibility and less complexity than the SD+FEC scheme

This paper deals with synchronization, channel estimation, and detection in ultrawideband (UWB) biphase impulse-modulated systems. We address both the single-user scenario, and the multiuser scenario assuming a direct-sequence code-division multiple-access (DS-CDMA) scheme. The users' binary-codeword elements modulate short-duration pulses. Codewords span a transmission frame. Frames are separated by a guard time to cope with the channel time dispersion. The detection approach is single-user-based and it operates in the frequency domain (FD). The algorithm first acquires frame synchronization with the desired user. It runs a discrete Fourier transform (DFT), and it performs FD channel estimation for the desired user via a recursive least-squares (RLS) algorithm. Finally, detection is directly accomplished in the FD. Frame synchronization is achieved in the time domain with a two-step procedure that first acquires coarse timing, then finely estimates where the desired user's signal energy is located. In the presence of multiple-access interference (MAI), the algorithm is appropriately modified to include the capability of canceling the interference through the exploitation of its FD correlation. Simulation results show that the proposed approach exhibits fast convergence, and high performance with and without synchronous/asynchronous MAI.

Because of their interactive nature, multimedia streams must be sent over UDP and suitable countermeasures for minimizing the effect of data loss have to be taken. Among the various proposed techniques, coding with redundant filter banks has been proposed as a mean to add robustness to the data stream. In order to avoid the computationally expensive matrix inversion necessary for signal reconstruction in the presence of packet loss, an iterative reconstruction algorithm can be used. Unfortunately, the classical algorithm for iterative reconstruction does not necessarily converge if too many coefficients are lost. In this paper we propose a modified version which converges even in the case of excessive losses.

This paper presents the investigation carried out for the MetaVision project in the area of lossless compression for digital cinema. The goal of this investigation was to identify, among the many approaches, the one that is most suited to the MetaVision requirements. An extensive simulation campaign has thus identified JPEG-2000 as the best compromise between efficiency, standardization and lossy-to-lossless performance. This result was hardly predictable, especially because of the peculiar kind of material to be coded.

In this work, we propose a multiple description (MD) coding system for video streams. In particular, our scheme originates four descriptions from the spatially downsampled polyphase components of the original frames. Each description is compressed independently with the recent H264/AVC video coding standard, it is packetized and sent over an error prone network. In case of errors in one or more descriptions, appropriate concealing is applied at the receiver, before insertion of the corrected frames into the corresponding receiver frame buffers. We propose and compare different concealment solutions and a post processing stage to attenuate visual effects related to MD coding. We analyze the trade off between robustness to channel errors and coding efficiency, comparing the proposed technique with single description (SD) video coding with H264/AVC. Experimental results validate the effectiveness of the proposed scheme.

In this paper we propose a system based on multiple description coding (MD) to stream video over lossy or variable bandwidth networks. MD coding can be integrated in several ways into existing state-of-the-art video codecs, as MPEG or the new H.264. The proposed scheme is based on polyphase downsampling in the pixel domain, followed by independent coding of descriptions by means of existing video codecs. Other schemes are cited for comparison. For the proposed scheme, several enhancements are discussed: frames expansion, joint encoding and joint decoding. We compare the coding efficiency and the robustness to losses and errors with respect to standard and error-resilient single description coding (SD).

In this work, we present an algorithm for face 3-D motion estimation in videoconference sequences. The algorithm is able to estimate both the position of the face as an object in 3-D space (global motion) and the movements of portions of the face, like the mouth or the eyebrows ( local motion). The algorithm uses a modified version of the standard 3-D face model CANDIDE. We present various techniques to increase robustness of the global motion estimation which is based on feature tracking and an extended Kalman filter. Global motion estimation is used as a starting point for local motion detection in the mouth and eyebrow areas. To this purpose, synthetic images of these areas (templates) are generated with texture mapping techniques, and then compared to the corresponding regions in the current frame. A set of parameters, called action unit vectors (AUVs) influences the shape of the synthetic mouth and eyebrows. The optimal AUV values are determined via a gradient-based minimization procedure of the error energy between the templates and the actual face areas. The proposed scheme is robust and was tested with success on sequences of many hundreds of frames.

In this paper, we consider the problem of lossless compression of video by taking into account temporal information. Video lossless compression is an interesting possibility in the line of production and contribution. We propose a compression technique which is based on motion compensation, optimal three-dimensional (3-D) linear prediction and context based Golomb-Rice entropy coding. The proposed technique is compared with 3-D extensions of the JPEG-LS standard for still image compression. A compression gain of about 0.8 bit/pel with respect to static JPEG-LS, applied on a frame-by-frame basis, is achievable at a reasonable computational complexity.

In this paper, we consider the problem of lossless compression of video by taking into account temporal information. Video lossless compression is an interesting possibility in the line of production and distribution. We propose a compression technique which is based on motion compensation, optimal 3D linear prediction and context based Golomb-Rice entropy coding. The proposed technique is compared with 3D extensions of the JPEG-LS standard for still image compression. A compression gain of about 0.8 bit/pel with respect to static JPEG-LS, applied on a frame-by-frame basis, is achievable at a reasonable computational complexity.

We present an algorithm for face 3D motion estimation in videoconference scenes. The algorithm uses a modification of the CANDIDE face model and is based on feature tracking and the extended Kalman filter (EKF). Various techniques are adopted to increase the robustness of the feature tracking procedure and, in particular, a filtering technique on reference blocks. Global motion estimation is used as a starting point for local motion detection. To this purpose, we generate, by texture mapping, a synthetic image of the mouth, whose shape is changed using a set of action units (AU). The optimal AU values are determined via a gradient-based minimization procedure of the error energy between the template and the actual mouth image. The proposed scheme is quite robust and was tested with success on long sequences.

In a JPEG decoder, the quantized discrete cosine transform (DCT) coefficient blocks are usually processed using the inverse DCT to reconstruct an approximation of the original image. In the presence of quantization of the transform coefficients, the use of the inverse DCT in the decoder is not the best choice: We propose a procedure to design a family of separable 2D synthesis filters that minimize the reconstruction error power and these filters are used in the decoder instead of the inverse DCT. The appropriate reconstruction filters are selected on the basis of the standard quantization information provided in the JPEG bit stream. We show that the proposed decoding method gives some gain with respect to the standard decoder, making use of only the information provided by the JPEG bit stream.

Transform coding is a technique used worldwide for image coding, and JPEG has become the most common tool for image compression. In a JPEG decoder, the quantized transform coefficient blocks are usually processed using the inverse discrete cosine transform (DCT) in order to reconstruct an approximation of the original image. The direct and inverse DCT pair can be arranged in the form of a perfect reconstruction filter bank, and it can be shown that, in the presence of quantization of the transform coefficients, the perfect reconstruction synthesis is not the best choice. In this paper, we propose a procedure for the design of separable 2-D synthesis filters that minimize the reconstruction error power for transform coders. The procedure is used to design a family of filters which are used in the decoder instead of the inverse DCT. The appropriate reconstruction filters are selected on the basis of the standard quantization information provided in the JPEG bit stream. We show that the proposed decoding method gives some gain with respect to the usual decoder in most cases, Moreover, it only makes use of the standard information provided by a JPEG bit stream.

A technique is proposed for the automatic detection of spikes in electroencephalograms (EEG). A multiresolution approach and a non-linear energy operator are exploited. The signal on each EEG channel is decomposed into three subbands using a non-decimated wavelet transform. Each subband is analyzed by using a non-linear energy operator, in order to detect peaks. A decision rule detects the presence of spikes in the EEG, relying upon the energy of the three subbands. The effectiveness of the proposed technique was confirmed by analyzing both test signals and EEG layouts.

Subband coding is a popular and well established technique used in visual communications, such as image and video transmission. In the absence of quantization and transmission errors, the analysis and synthesis filters in a subband coding scheme can be designed to obtain perfect reconstruction of the input signal, but this is no longer the optimal solution in the presence of quantization of the subband coefficients. We presuppose the use of a two-dimensional (2-D) separable subband scheme and we address the problem of designing, for a given analysis filter bank and assuming uniform quantization of the subband coefficients, the set of row and column synthesis filters that minimize the mean squared reconstruction error at the output of the subband system. Since the corresponding optimization problem is inherently nonlinear, we propose a suboptimal solution that extends a one-dimensional (l-D) optimal filter design procedure, already presented in the literature, to a 2-D separable synthesis filter bank. The separable 2-D extension is not trivial, since the processing in one direction, e.g., the rows, alters the statistics of the signals for the design of the filters in the other direction, e.g., the columns. To further simplify the filter design, we propose to model the input image as a 2-D separable Markov process plus an additive white component. Several design examples using both synthetic signals and real world images are presented, showing that the filters designed using the proposed technique can give a significant gain with respect to the perfect reconstruction solution, especially when the dither technique is used for quantization. The simulation results also show that the proposed image model can be conveniently used in the synthesis filter design procedure.

Three-dimensional (3-D) motion estimation is applied to the problem of motion compensation for video coding. We suppose that the video sequence consists of the perspective projections of a collection of rigid bodies which undergo a rototranslational motion. Motion compensation can be performed on the sequence once the shape of the objects and the motion parameters are determined. We show that the motion equations of a rigid body can be formulated as a nonlinear dynamic system whose state is represented by the motion parameters and by the scaled depths of the object feature points. An extended Kalman filter is used to estimate both the motion and the object shape parameters simultaneously. The inclusion of the shape parameters in the estimation procedure adds a set of constraints to the filter equations that appear to be essential for reliable motion estimation. Our experiments show that the proposed approach gives two advantages. First, the filter can give more reliable estimates in the presence of measurement noise in comparison with other motion estimators that separately compute motion and structure. Second, the filter can efficiently track abrupt motion changes. Moreover, the structure imposed by the model implies that the reconstructed motion is very natural as opposed to more common block-based schemes. Also, the parameterization of the model allows for a very efficient coding of the motion information.

The complexity of the MPEG video coding standard makes it a challenging task to provide simple strategies to tune the coder parameters in order to produce consistent quality images for a given bit budget. Pre-quantization and pre-coding of individual frames have been proposed in the literature as a viable solution to the problem. In this paper, we show that a simple statistical model for the DCT coefficients can be used to select quantization parameters at the macroblock level. In particular, 8/spl times/8 blocks of DCT coefficients are modelled as i.i.d. random vectors of independent variables. The rate-distortion performance associated to a given quantizer can be calculated from the model and the quantizer parameters can be locally controlled to meet the specified rate-distortion target. The experimental results show that, with a limited complexity, it is possible to achieve a great precision in the bit rate control without actual pre-coding.

We develop an adaptive scheme for quantization of subband or transform coded frames in a typical video sequence coder. Using a generalized Gaussian model for the subband or transform coefficients, we present a procedure to determine the optimum dead-zone quantizer for a given entropy of the quantizer output symbols. We find that, at low bit rates, the dead-zone quantizer offers better performance than the uniform quantizer. The model is used to develop an adaptive procedure to update the quantizer parameters on the basis of the state of a channel buffer with constant output rate and variable input rate. We compare the accuracy of the generalized Gaussian model in predicting the actual bit rate to that achievable using the simpler and more common Laplacian model. Experimental results show that the generalized Gaussian model has superior performance than the Laplacian model, and that it can be effectively used in a practical scheme for buffer control.

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

In this work, statistical based motion estimation is applied to the problem of motion estimation for video coding. We show that the motion equations of a rigid body can be formulated as a nonlinear dynamic system whose state is represented by the motion parameters and by the scaled depths of the object feature points. An extended Kalman filter is used to estimate the global motion, from which successive frames can be predicted in a motion compensated video coding system. The structure imposed by the model implies that the reconstructed motion is very natural in comparison to more common block-based schemes. Moreover, the parametrization of the model allows for a very efficient coding of motion information.

A procedure to evaluate the coding gain for 2-D subband systems is explicitly presented. The technique operates in the signal domain and requires the knowledge of the input process auto-correlation function. Both the case of uniform subband and pyramid decomposition are considered. In the case of a separable input process spectrum, the evaluation can be performed by considering appropriately defined 1-D systems, thus, making the procedure very convenient in terms of computational complexity. Using a model that has been recently derived for difference images in motion-compensated image sequence coders, we compare the performance of several filter banks and transform coders in terms of coding gain and asymptotic rate-distortion figures. The results for intraframe and interframe coding show that uniform subband coders can have a performance superior to that of transform coders. Pyramidal schemes appear to have a slightly worse performance.

We propose a coding technique that is based on the generalized block prediction of the multiresolution subband decomposition of motion compensated difference image frames. A segmentation mask is used to distinguish between the regions where motion compensation was effective and those regions where the motion model did not succeed. Each difference image is decomposed into a multiresolution pyramid of subbands where the highest resolution subbands are divided into two regions, based on the information given by the segmentation mask. Only the coeffcients of the regions corresponding to the motion model failure are considered in the highest resolution subbands. The remaining coefficients are coded using a multiresolution vector quantization scheme that exploits inter-band non-linear redundancy.

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG.

In this work, we propose a hybrid vector quantization scheme for general images that aims at exploiting similarities among detail signals in a multiresolution decomposition of the image. In a multiresolution decomposition, the image is decomposed into a set of subbands which are localized in scale, orientation and space. Our coding scheme consists of dividing each subimage of the multiresolution decomposition into square range blocks. The range blocks are matched against domain blocks chosen in the lower resolution subimage with the same orientation, and coded through a description of the map transforming the domain block into the range block. The pool of of domain blocks acts as a codebook for the range block, as in vector quantization, with the difference that the codebook is built from blocks inside the multiresolution decomposition. If the prediction procedure is not satisfactory with respect to a target quality, the block is coded using a geometric vector quantizer for Laplacian random variables.>

In this work, we devise a wavelet-fractal coder where the causal similarity among blocks of different subbands in a wavelet decomposition of the image is exploited. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. The experimental results show that the performance of our scheme is superior, for both visual quality and MSE, to that obtainable with standard fractal block coders and also to that of other popular image coders like JPEG.>

In this paper we consider an inverse problem and an approximation problem for fractal images. The inverse problem involves finding the Iterated Function System (IFS) parameters for a class of signals that are exactly generated via an IFS. We make use of the wavelet transform and of the image moments to solve the inverse problem. The approximation problem involves finding a fractal IFS generated image whose moments either match exactly or in a mean squared error sense a range of moments of the original image. The approximating measures are generated by an IFS model of a special form, and provide a general basis for the approximation of arbitrary images. Experimental results verifying our approach are presented.>

The geometry of fractals is rich enough that they have extensively been used to model natural phenomena and images. Iterated function systems (IFS) theory provides a convenient way to describe and classify deterministic fractals in the form of a recursive definition. As a result, it is conceivable to develop image representation schemes based on the IFS parameters that correspond to a given fractal image. In this paper, we consider two distinct problems: an inverse problem and an approximation problem. The inverse problem involves finding the IFS parameters of a signal that is exactly generated via an IFS. We make use of the wavelet transform and of the image moments to solve the inverse problem. The approximation problem involves finding a fractal IFS-generated image whose moments match, either exactly or in a mean squared error sense, a range of moments of the original image. The approximating measures are generated by an IFS model of a special form and provide a general basis for the approximation of arbitrary images. Experimental results verifying our approach will be presented.

Fractal geometry has provided statistical and deterministic models for classes of signals and images that represent many natural phenomena and objects. Iterated function systems (IFS) theory provides a convenient way to describe and classify deterministic fractals in the form of a recursive definition. As a result, it is conceivable to develop image representation schemes based on the IFS parameters that correspond to a given fractal image. The authors propose the use of the wavelet transform and of the moment method for the solution of the inverse problem of recovering IFS parameters from fractal images. The redundancy of a fractal with respect to scale variation is mirrored by its wavelet decomposition, thus providing a method to estimate the scaling parameters for a class of IFSs modeling the image. Displacement parameters and probabilities are then found using the moment method. Experimental results verifying the approach are presented.>

Frequency domain techniques are presented for the evaluation of the autocorrelation in the case of multidimensional sequences. The analysis confirms that the use of the proposed frequency domain techniques leads to significant computation time savings, extremely valuable in practical contexts.>