While the phylogenetic analysis of multimedia documents keeps being investigated, some recent studies have shown the possibility of re-using the same strategies to analyze the evolution of computer programs (Software Phylogeny), considering its many applications spanning from copyright enforcement to malware detection. This paper presents a solution for reconstructing the phylogenetic dependencies of different releases of a given program. The proposed method collects cache miss statistics during the program execution, builds a dissimilarity matrix from the results, and then estimates the corresponding Software Phylogenetic Tree (SPT) using a minimum spanning tree algorithm.

The recent development of multimedia has made video editing accessible to everyone. Unfortunately, forensic analysis tools capable of detecting traces left by video processing operations in a blind fashion are still at their beginnings. One of the reasons is that videos are customary stored and distributed in a compressed format, and codec-related traces tends to mask previous processing operations. In this paper, we propose to capture video codec traces through convolutional neural networks (CNNs) and exploit them as an asset. Specifically, we train two CNN s to extract information about the used video codec and coding quality, respectively. Building upon these CNN s, we propose a system to detect and localize temporal splicing for video sequences generated from the concatenation of different video segments, which are characterized by inconsistent coding schemes and/or parameters (e.g., video compilations from different sources or broadcasting channels). The proposed solution is validated using videos at different resolutions (i.e., CIF, 4CIF, PAL and 720p) encoded with four common codecs (i.e., MPEG2, MPEG4, H264 and H265) at different qualities (i.e., different constant and variable bitrates, as well as constant quantization parameters).

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, image and video compression makes object detection more difficult since such operations erase most of the computed visual features. In this paper we propose a video rate control strategy that exploits a saliency metric to identify image regions where features are likely to be found. According to the pixel statistics, the approach decides whether to increase the coding quality or include some side information that specifies the keypoint locations. Experimental results on HEVC coder show that the proposed rate control algorithm improves both the detection accuracy and the rate-distortion performance with respect to state-of-the-art strategies.

Recent works have shown how multiple description coding proves to be an effective solution for multimedia streaming over peer-to-peer (P2P) and content delivery networks (CDNs). However, the presence of losses and congestions throughout the network affects the visual quality of the reconstructed sequence at the end terminal. These inconveniences can be mitigated by specifying different levels of Quality of Service, but an optimal packet classification is hard to obtain since P2P and CDN protocols operate at higher protocol layers (ignoring network conditions of the lowest stages), the network can be quite distributed and little information can be available regarding other network segments involved in the transmission. The peculiarities of the transmission scenario require a distributed and robust packet classification strategy that grants both intra-stream and inter-stream diversities among the loss patterns for the different streams. The classification approach presented here is characterized via a noncooperative game, where the different uploading nodes are players/descriptions competing for the allocation of the available network resources. Each player may switch from a selfish strategy to a more cooperative strategy according to its convenience (opportunistic players). Experimental results show that the proposed solution proves to be quite effective under different network scenarios.

Block based recursive search for motion estimation is already suitable for real time applications thanks to its wide utilization, but the increase of resolution in modern displays and the need for real time software implementations demand for faster approaches. In this paper a new motion estimation procedure that has been developed for a parallel implementation is presented. The parallel implementation was achieved by avoiding the spatial recursion which is typical for the recursive search systems, while the convergence can still be obtained by means of the temporal recursion only. In particular the use of a binarized cross correlation as a matching cost criteria instead of the standard SAD permits an improved motion vector field and a faster convergence without increasing the computational cost. The experimental results show a similar vector field estimation in comparison to the standard SAD based recursive search method with the considerable advantage of a possible parallel implementation.

Typical consumer cameras sense at each pixel only one out of the three color components the representation of a color image requires. Then the missing components are estimated via a procedure referred to as demosaicking. The recent spread of sparse regularization approaches for signal reconstruction purposes has extended to demosaicking algorithms too. In this paper, starting from a sparse representation based demosaicking algorithm recently appeared in the literature, a new one is devised. The proposed algorithm effectively estimates the original luminance component from the acquired data and then uses it to guide the sparsity based reconstruction of the full-resolution image. This hybrid approach to demosaicking allows the new algorithm to outperform the past one and to compete with leading demosaicking algorithms, both in terms of PSNR measure and visual quality.

Among all the techniques for 3D acquisition, stereo vision systems are the most common. More recently, Time-of-Flight (ToF) range cameras have been introduced. They allow real-time depth estimation in conditions where stereo does not work well. Unfortunately, ToF sensors still have a limited resolution (e.g., 200 × 200 pixels). The goal of this paper is to combine the information from the ToF with one or two standard cameras, in order to obtain a highresolution depth image. To this end, we propose a new bilateral filter for depth maps up-sampling, which exploits the additional information provided by a high-resolution color image. Moreover, we present an entire framework for the super-resolution of a real ToF depth map with a single camera color image. The algorithm allows to enhance the resolution of the ToF camera depth image up to 1920 × 1080 pixels.

In this paper we propose an effective de-ringing method for DCT compressed images and videos. This method performs a subband decomposition provided by a translation invariant wavelet and processes only the high level subbands by means of a local adaptive equalization around the edges of the image. This makes the procedure suitable for both texture and flat areas, and moreover for different compression ratios. The benefits of the algorithm are then shown with objective, based on PSNR and SSIM metrics, and subjective evaluations.

Infrared structured light sensors are widely employed for control applications, gaming, acquisition of dynamic and static 3D scenes. Recent developments have lead to the availability on the market of low-cost sensors which prove to be extremely sensitive to noise, light conditions, materials, the surface nature of the objects, and their distance from the camera. As a matter of fact, accurate denoising and interpolation strategies are needed. The paper presents a quality enhancement strategy for depth maps targeting low-cost IR structured light sensors. The approach has been tested using the MS Xbox Kinect device in both indoor and outdoor scenarios under different light conditions.

Traditional video compression is based on block-based motion estimation and compensation. However, experimental results and the latest standards have proved that adapting the size and the shape of the motion estimation area to the objects in the scene significantly improves the performance of the overall coding process. The current paper presents a new segmentation-based coding strategy that partitions the input frame into irregularly-shaped regions. These segments are then used to estimate the motion-compensated prediction in the previous frames. The scheme outperforms the rate-distortion performance of H.264/AVC of 2 dB with a reasonable increment of the encoding complexity due to segmentation.

Recent works have shown how Multiple Description Coding (MDC) and Peer-to-Peer transmission protocols prove to be extremely helpful in streaming video sequences. The quality of the reconstructed sequence can be significantly improved by differentiating the Quality-of-Service levels via distributed packet classifications performed by each peer independently. The packet labelling can be modelled via noncooperative games where the different uploading nodes are players/descriptions competing for the available network resources. The paper shows how this framework can be effectively employed for the transmission of 3D video sequences adopting an FEC-based multiple description scheme that encapsulates different kinds of data streams (color information, geometry) into a set of equally-important packets. Experimental results show that the classification strategy based on game theory improves the quality of the reconstructed sequence with respect to standard classification strategies.

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions.

Future multimedia applications will rely on the transmission of 3D video contents within heterogeneous fruition scenarios, and as a matter of fact, the reliable delivery of 3D video signals proves to be a crucial issue in such communications. To this purpose, multimedia communication experts have been designing cross-layer strategies to improve the quality of the perceived 3D experience. This article presents a new cross-layer strategy, called Cognitive Source Coding (CSC), that defines a new 3D video system able to identify the different elements of the 3D scene and choose the most appropriate coding strategy.

Reliable delivery of 3D video contents to a wide set of users is expected to be the next big revolution in multimedia applications provided that it is possible to grant a certain level of Quality-of-Experience (QoE) to the end user.

During the last years, video coding designers have proposed robust coding approaches that combine Multiple Description Coding (MDC) schemes with Distributed Video Coding (DVC) principles. In this way, it is possible to obtain a better error resilience since the distortion drifting through the sequence is significantly mitigated by the DVC coding unit. The paper presents a Multiple Description Distributed Video Coder (MDDVC) that codes the input video signal generating a set of "lossy" syndromes for each pixel block and creates different descriptions multiplexing primary and redundant video packets. Experimental results show that at high loss probabilities the proposed solution improves the results of the original MDC approach.

An efficient compression of depth maps proves to be a crucial element in the transmission and storage of 3-D scenes. However, the peculiarities of geometry information make the traditional coding paradigms for natural images less effective for the coding of depth images. The letter presents a novel coding scheme that employs an oversegmentation of the input depth image into a huge set of small regions. These regions are then fused together according to the target number of objects that the algorithm needs to identify in the representation. This procedure is iterated more than once generating several refinement layers that permit obtaining a progressively-increasing quality in the scene. Experimental results show that in most cases the proposed approach reaches a better coding performance with respect to previous coding methods.

Spatially scalable image coding algorithms are mostly based on linear filtering techniques that give a multi-resolution representation of the data. Reversible cellular automata can be instead used as simpler, non-linear filter banks that give similar performance. In this paper, we investigate the use of reversible cellular automata for lossy to lossless and spatially scalable coding of smooth multi-level images, such as depth maps. In a few cases, the compression performance of the proposed coding method is comparable to that of the JBIG standard, but, under most test conditions, we show better compression performances than those obtained with the JBIG or the JPEG2000 standards. The results stimulate further investigation into cellular automata-based methods for multi-level image compression.

In this work, we have designed an efficient arithmetic coder for the non-linear bi-level image coder based on reversible cellular automata transform reported in the work of Cruz-Reyes and Kari (2008). The proposed approach relies on non-linear transform operations based on RmbCA, decomposing the image into four subimages (named LL, LH, HL, HH in lexicographic order) in such a way that the LL subimage represents a low resolution version of the original image. Performing an edge detection on this subimage, it is possible to identify the coordinates where there is a high probability that the other subimages present a black pixels. In this way it is possible to minimize the amount of data that have to be processed reducing the coded bit stream. In order to increase the probability that black pixels lie in the selected region, the coding algorithm enlarges the identified region according to the values of Sobel operators computed on the LL subimage.

Reversible cellular automata have been proposed as nonlinear filter banks for spatially scalable binary image compression. Previous design approaches led to efficient coders for simple images but do not guarantee detail preservation across scales. In this work we are concerned with the design of optimized cellular automata rules that offer better performances. The lifting principle is used for guaranteeing reversibility. We obtain a scalable coder that (i) offers the same quality of JBIG in the reduced scale images, (ii) achieves high compression ratios on simple images, (iii) provides at least the same performance of JBIG on any image content, and (iv) employs arithmetic coding with a very small number of contexts for reduced complexity.

In a digital camera usually images are captured using a Color Filter Array (CFA), requiring a demosaicking process to reconstruct the full resolution color image. Often also a denoising step is necessary to remove the noise introduced by the sensor. In this paper we analyze demosaicking and denoising jointly. The color reconstruction is performed using space-varying filters, therefore an evaluation of the autocorrelation of the noise after a space varying filtering is reported. The proposed approach gives good performances and the computational cost is kept low.

Recent works have shown how Multiple Description Coding (MDC) proves to be an effective solution for the multimedia transmission over Peer-to-Peer and Content Delivery Networks. However, despite the flexibility and scalability of these novel transmission paradigms, packet streams are still affected by losses, and an effective packet labelling that differentiates the QoS levels for the transmitted data is required. The paper presents a Game Theory based approach to classify the MDC coded video packets that are downloaded from a set of terminals spread throughout the network. The proposed approach improves the quality of the reconstructed sequence with limited feedback information from the network.

Demosaicking is the process of reconstructing a full resolution color image from the sampled data acquired by a digital camera that apply a color filter array to a single sensor. In this paper, we propose a regularization approach to demosaicking, making use of some prior knowledge about natural color images, such as smoothness of each single color component and correlation between the different color channels. Initially, a quadratic strategy is considered and a general approach is reported. Then, an adaptive technique is analyzed, in order to improve the reconstruction near the edges and the discontinuities of the image. This is performed using a novel strategy that avoids computational demanding iterations. The proposed approach provides good performances and candidates itself for many applications. Moreover, since the response of the pixel sensors can be taken into account, it can handle nonideal acquisition devices.

Recent years have witnessed a rapid increment in video applications over wireless networks including on-demand video streaming and video phoning. This growth has also brought the need to find a good compromise in the conflict between resource limitations affecting mobile devices and the desire for high-quality multimedia services. It is possible to face this problem adopting a cross-layer strategy that jointly tunes the parameters of each layer in the network protocol stack. In this optimization strategy, complexity is one of the most significant issues because of the limited computational resources and power supply. The paper presents a low-complexity cross-layer algorithm that is able to jointly tune the parameters of different protocol layers by adopting simple but effective models. The quality of the reconstructed video sequence, the produced bit rate, and the service class associated to each packet are seen as functions of the percentage of null DCT coefficients. This modeling permits to find a closed-form solution to the joint optimization problem that can be computed with a limited number of operations and grants, at the same time, a good visual quality in the reconstructed sequence.

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.

In this paper, we present a novel technique that uses the optimal linear prediction theory to exploit all the existing redundancies in a color video sequence for lossless compression purposes. The main idea is to introduce the spatial, the spectral, and the temporal correlations in the autocorrelation matrix estimate. In this way, we calculate the cross correlations between adjacent frames and adjacent color components to improve the prediction, i.e., reduce the prediction error energy. The residual image is then coded using a context-based Golomb-Rice coder, where the error modeling is provided by a quantized version of the local prediction error variance. Experimental results show that the proposed algorithm achieves good compression ratios and it is roboust against the scene change problem.

Summary form only given. In this paper we present a lossless compression algorithm for colour video sequence which exploits the spatial, the spectral and the temporal correlations of a colour video sequence in the RGB colour space using the well-known optimal prediction theory. The main idea is to construct the optimal prediction coefficients estimating an autocorrelation matrix which exploits all these correlations. No colour transformation or motion compensation are applied because reversible colour transformations are not able to fully decorrelate the three bands of each frame, and motion compensation remarkably increases the complexity of the updating step of the autocorrelation matrix estimate. Furthermore, the fact that our algorithm is not based on motion compensation lead it to be robust to scene changes. The prediction errors are then coded using a context-based Golomb-Rice coder, with bias cancellation, but without run-length coding. To construct the contexts, the prediction errors are then modeled using an estimate of their local variance. This estimate considers all the previous prediction errors, using a forgetting factor to improve the adaptability of the proposed algorithm. The quantized estimated variance values are used as contexts for the Golomb-Rice coder, and, among others, we considered the following solutions: - 12 contexts estimated by sampling the standard deviation with a quantization step Delta = 1, and a saturation threshold equal to 12, [delta12]; - 128 contexts estimated by sampling the standard deviation with Delta = 1/3 and a saturation threshold equal to 128/3, [delta128]. The obtained coding results are presented for the proposed algorithm compared to JPEG-LS (without using any colour transformation), and JPEG2000 (in lossless mode, using the reversible colour YDbDrtransform, and the 5/3 DWT). The results show an improvement of about 1.5 bpp and 0.65 bpp if compared with JPEG-LS coder, and JPEG2000, respectively

Most color digital cameras apply a color filter array to capture the scene, requiring an interpolation of the subsampled color components to obtain the full resolution image. Therefore, if reconstruction is not performed correctly, noticeable artifacts are produced, which result more evident in the detailed parts of the image. In this paper we propose a demosaicing algorithm based on directional filtering that uses a novel approach to locate the details of the image. To this purpose, edge-estimation is performed on the luminance component of the image in order to give a more reliable information, and the properties of the wavelet transform are used to estimate the edge directions. Experimental results proved the effectiveness of this approach, giving high performance in PSNR, and good estimates of image details too.

Most digital cameras use a color filter array to capture the colors of the scene. Downsampled versions of the red, green, and blue components are acquired, and an interpolation of the three colors is necessary to reconstruct a full representation of the image. This color interpolation is known as demosaicing. The most effective demosaicing techniques proposed in the literature are based on directional filtering and a posteriori decision. In this paper, we present a novel approach to this reconstruction method. A refining step is included to further improve the resulting reconstructed image. The proposed approach requires a limited computational cost and gives good performance even when compared to more demanding techniques.

In this paper we present a new way to exploit optimal vector prediction theory for lossless compression of color images. To compress color images, the spectral correlation is usually reduced using a color space transformation (e.g., from RGB colour space to YUV Ricoh colour space, or to YCbCr when small losses are allowed). In this work, we exploit the spectral correlation to develop an optimal vector predictor in order to reduce the entropy of the residual image. To this purpose, we consider a pixel as a vector of the three components R, G, and B, and we predict this vector. As a result, we obtain an improvement of the compression ratio at the cost of an increase in the computational complexity. Some techniques to reduce the computational cost are presented. A comparison is carried out with scalar version of GLICBAWLS and JPLG-LS.

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 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.

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.>

The authors introduce an algorithm for simultaneous magnitude and group-delay design of FIR (finite impulse response) filters, a multiple criterion optimization problem for which no characterization theory is presently available. The algorithm, derived from differential correction, has global convergence. An experimental comparison with a method for complex polynomial approximation, a problem whose mathematical characterization is known, yields very useful and consistent results. The authors compare the results of two filter design formulations which are conceptually similar but formally different, and they present data potentially useful for the mathematical characterization of simultaneous polynomial approximation in magnitude and group-delay.>

The problem of reconstructing a bandlimited signal from a set of nonuniformly spaced samples is addressed, with particular emphasis on a method due to J.L. Yen (1956). A simple proof for the derivation of Yen's interpolation formula is given. The interpolating process is then modeled as a linear time-varying system and is analyzed in the frequency domain by means of its bifrequency transmission function (BFTF). It is proven that Yen's interpolator can be obtained as a special case of an optimal frequency-domain design of its BFTF using a weighted least-square criterion. A practical interpolator, based on Yen's method, is considered for situations where the number of samples is large or infinite and the original Yen interpolation formula is inapplicable. The performance of this modified Yen interpolator is compared with that of other commonly used interpolation methods in terms of achievable S/N.>