NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT's successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.

Abstract Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.

We propose a neuroevolutionary speciation mechanism that is applied on NeuroEvolution of Augmenting Topologies (NEAT) that solely evolves neural networks' topology and weights and its extension HA-NEAT that also evolves activation functions. The new speciation mechanism is defined based on the behavior of the individuals rather than their topological similarity. Focusing on classification tasks we build artificial datasets of high complexity. Performance is described by (i) median classification accuracy, (ii) computational efficiency (number of generations) and (iii) network complexity (number of nodes and connections). The performance metrics are compared using Kruskal-Wallis hypothesis tests with Bonferroni correction. It is found that the proposed behavioral speciation mechanism outperforms the original speciation solving problems that were not solvable before or improving the accuracy and reducing the network complexity.

In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.

In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

Post-mastectomy lymphedema is a condition that is present in many women after treatment for breast cancer. It causes physical discomfort, and when prolonged on the long term, results in the formation of fibrotic tissue in the edematous arm. A novel approach to analyze CT images of the arms and to assess the edematous components is proposed in this work. Histogram analysis is performed at different depths of the fat layer in the arm. The results show that the proposed method is useful in differentiating between the normal and the edematous arm, and in detecting fibrotic components in the edematous arm. The preliminary findings, obtained on nine patients, reveal promising new histogram features for the detection of edema and fibrosis and still have to be developed further in a future study.

Our recently proposed wavelet-based L-infinite-constrained coding approach for meshes ensures that the maximum error between the vertex positions in the original and decoded meshes is guaranteed to be lower than a given upper bound. Instantiations of both L-2 and L-infinite coding approaches are demonstrated for MESHGRID, which is a scalable 3D object encoding system, part of MPEG-4 AFX. In this survey paper, we compare the novel L-infinite distortion estimator against the L-2 distortion estimator which is typically employed in 3D mesh coding systems. In addition, we show that, under certain conditions, the L-infinite estimator can be exploited to approximate the Hausdorff distance in real-time implementations.

This paper proposes novel scalable mesh coding designs exploiting the intraband or composite statistical dependencies between the wavelet coefficients. A Laplacian mixture model is proposed to approximate the distribution of the wavelet coefficients. This model proves to be more accurate when compared to commonly employed single Laplacian or generalized Gaussian distribution models. Using the mixture model, we determine theoretically the optimal embedded quantizers to be used in scalable wavelet-based coding of semiregular meshes. In this sense, it is shown that the commonly employed successive approximation quantization is an acceptable, but in general, not an optimal solution. Novel scalable intraband and composite mesh coding systems are proposed, following an information-theoretic analysis of the statistical dependencies between the coefficients. The wavelet subbands are independently encoded using octree-based coding techniques. Furthermore, context-based entropy coding employing either intraband or composite models is applied. The proposed codecs provide both resolution and quality scalability. This lies in contrast to the state-of-the-art interband zerotree-based semiregular mesh coding technique, which supports only quality scalability. Additionally, the experimental results show that, on average, the proposed codecs outperform the interband state-of-the-art for both normal and nonnormal meshes. Finally, compared with a zerotree coding system, the proposed coding schemes are better suited for software/hardware parallelism, due to the independent processing of wavelet subbands.

The paper investigates the novel concept of local-error control in mesh geometry encoding. In contrast to traditional mesh-coding systems that use the mean-square error as target distortion metric, this paper proposes a new L-infinite mesh-coding approach, for which the target distortion metric is the L-infinite distortion. In this context, a novel wavelet-based L-infinite-constrained coding approach for meshes is proposed, which ensures that the maximum error between the vertex positions in the original and decoded meshes is lower than a given upper bound. Furthermore, the proposed system achieves scalability in L-infinite sense, that is, any decoding of the input stream will correspond to a perfectly predictable L-infinite distortion upper bound. An instantiation of the proposed L-infinite-coding approach is demonstrated for MESHGRID, which is a scalable 3D object encoding system, part of MPEG-4 AFX. In this context, the advantages of scalable L-infinite coding over L-2-oriented coding are experimentally demonstrated. One concludes that the proposed L-infinite mesh-coding approach guarantees an upper bound on the local error in the decoded mesh, it enables a fast real-time implementation of the rate allocation, and it preserves all the scalability features and animation capabilities of the employed scalable mesh codec.

The paper thoroughly validates the proposed SID model on a broad dataset, showing significant accuracy improvements over SII models. Additionally, it is theoretically demonstrated that DVC systems that make SII assumptions suffer a performance penalty depending entirely on the correlation statistics of the video data.

Current models in Wyner-Ziv video coding consider the temporal correlation noise to be side-information independent (SII). This paper goes beyond this assumption and proposes a novel model, of which the parameters are side-information dependent (SID). The proposed model is experimentally validated showing remarkable accuracy improvement over the conventional SII model. Moreover, a novel SID technique for the accurate estimation of the correlation channel in video is introduced. The proposed technique enables the design of a novel pixel-domain Wyner-Ziv video coding system operating without a feedback channel. Preliminary experimental results show that the proposed codec achieves superior performance compared to the state-of-the-art in pixel-domain Wyner-Ziv coding.

In this paper, a novel wavelet-based composite mesh coding scheme is presented. In contrast to the state-of-the-art scalable interband mesh codec, the proposed codec relies on composite dependency models that capture both the interband and intraband statistical dependencies between wavelet coefficients. Additionally, each wavelet subband is processed independently, which allows for parallelized processing and for a progressive reconstruction of each mesh resolution level. Compared to the state-of-the-art, the proposed codec yields for almost all rate points superior compression performance in L2-sense. Furthermore, the generated bitstreams are near-optimal in rate-distortion sense, which eliminates the need of a post-compression rate-distortion optimization technique.

It has been recently demonstrated that, in distributed video coding (DVC) side-information dependent modeling of the correlation channel brings significant performance gains over side-information independent assumptions. In this letter, we present a novel technique enabling advanced side-information dependent estimation of the correlation channel at the decoder starting from a very coarse knowledge of it. The proposed technique triggers the design of a spatial-domain DVC codec which enables the suppression of the feedback channel. Experimental results show that the proposed codec achieves similar performance compared to the state-of-the-art in transform-domain Wyner-Ziv video coding while still operating at a fraction of its encoding complexity.

The Nucleotide Genomic Signal (NuGS) methodology is based on the conversion of symbolic nucleotide sequences into genomic signals, allowing the use of signal processing methods to investigate genetic information. The approach is adequate for both the large scale analysis of genomes and for the monitoring of local variations in genomic sequences, such as those caused by pathogen variability or by genomic inserts. The inserts can comprise retro-viruses, individual genes, or non-coding segments. Even when the inserts are not fully active viruses, they can still retain a significant pathogenicity when encoding viral enzymes that can facilitate the dissemination of some viruses, generating an increased susceptibility to the contamination with these pathogens. Inserts occur usually in complementary pairs, with the tendency to restore the original regularities of the host genome. The paper illustrates the NuGS methodology for two typical cases: the insert of the SPBc2 bacillusphage complete genome in the genome of Bacillus subtilis and the inserts of genes from the PE-PGRS family in the genome of Mycobacterium tuberculosis.

This paper investigates the novel concept of local error control in arbitrary mesh encoding, and proposes a new L-infinite mesh coding approach implementing this concept. In contrast to traditional mesh coding systems that use the mean-square error as distortion measure, the proposed approach employs the L-infinite distortion as target distortion metric. In this context, a novel wavelet-based L-infinite-constrained coding approach for meshes is proposed, which ensures that the maximum local error between the original and decoded meshes is lower than a given upper-bound. Additionally, the proposed system achieves scalability in L-infinite sense, that is, the L-infinite distortion upper-bound can be accurately estimated when decoding any layer from the input stream. Moreover, a distortion estimation approach is proposed, expressing the L-infinite distortion in the spatial domain as a statistical estimate of quantization errors produced in the wavelet domain. An instantiation of the proposed L-infinite coding approach is demonstrated for MESHGRID, which is a scalable 3D object coding system, part of MPEG-4 AFX. The proposed L-infinite coding approach guarantees that the maximum error is upper-bounded, it enables a fast real-time implementation of the rate-allocation, and it preserves all the scalability features and animation capabilities of the employed scalable mesh codec.

A novel coding approach, applying open-loop coding principles in predictive coding systems is proposed in this paper. The proposed approach is instantiated with an intra-frame video codec employing the transform and spatial prediction modes from H.264. Additionally, a novel rate-distortion model for open-loop predictive coding is proposed and experimentally validated. Optimally allocating rate based on the proposed model provides significant gains in comparison to a straightforward rate allocation not accounting for drift. Furthermore, the proposed open-loop predictive codec provides gains of up to 2.3 dB in comparison to an equivalent closed-loop intra-frame video codec employing the transform, prediction modes and rate-allocation from H.264. This indicates that, with appropriate drift compensation, open-loop predictive coding offers the possibility for further improving the compression performance in predictive coding systems.

This paper proposes a novel joint source-channel coding (JSCC) methodology which minimizes the end-to-end distortion for the transmission over packet loss channels of scalable video encoded using SVC, the scalable extension of H.264/AVC. The proposed JSCC approach performs channel protection using low-density parity-check codes and relies on Lagrangian-based optimization techniques to derive the appropriate protection levels for each layer produced by the scalable source codec. Our JSCC approach for SVC can support spatial, temporal and quality scalability and can provide an optimized channel protection in any scalable setting. Experiments show that our JSCC methodology yields competitive results against state-of-the-art Lagrangian-based JSCC algorithms. Compared to the state-of-the-art, our approach significantly reduces the number of computations needed to derive the rate-distortion hulls. Moreover, the proposed approach constructs convex rate-distortion hulls for each frame, irrespective of the target rate. This allows the pre-computation of the convex rate-distortion hulls for typical packet loss channels, such that the extraction of a near-optimal JSCC allocation can be achieved on-the-fly for any target rate or packet-loss rate. We conclude that the proposed JSCC methodology provides optimized resilience against transmission errors in scalable video streaming over variable-bandwidth error-prone channels.

This paper proposes a new approach for joint source and channel coding (JSCC) of meshes, simultaneously providing scalability and optimized resilience against transmission errors. An unequal error protection approach is followed, to cope with the different error-sensitivity levels characterizing the various resolution and quality layers produced by the input scalable source codec. The number of layers and the protection levels to be employed for each layer are determined by solving a joint source and channel coding problem. In this context, a novel fast algorithm for solving the optimization problem is conceived, enabling a real-time implementation of the JSCC rate-allocation. An instantiation of the proposed JSCC approach is demonstrated for MeshGrid, which is a scalable 3-D object representation method, part of MPEG-4 AFX. In this context, the L-inflnite distortion metric is employed, which is to our knowledge a unique feature in mesh coding. Numerical results show the superiority of the L-inflnite norm over the classical L-2 norm in a JSCC setting. One concludes that the proposed joint source and channel coding approach offers resilience against transmission errors, provides graceful degradation, enables a fast real-time implementation, and preserves all the scalability features and animation capabilities of the employed scalable mesh codec.

Sets of related signals can be represented by separating their joint variation and showing the individual signal offsets with respect to this reference. An example is the genomic signal analysis of pathogen variability. The conversion of symbolic nucleotide sequences to genomic signals allows to use signal processing methods to analyze genomic data. This approach reveals striking regularities in the distribution of nucleotides and pair of nucleotides along the sequences, in both prokaryotes and eukaryotes. Genomic signals can also be used for sequence prediction, similarly to time series prediction. The methodology is also adequate for studying the development of pathogen multiple resistance to drugs.

Distributed video coding is a coding paradigm that allows complexity to be shared between encoder and decoder. In this context, video coding systems have been developed with encoder complexities similar to H.263+ intra-coding, while obtaining compression performance comparable to H.263+ inter coding. The decoders in these systems typically employ motion-compensated frame interpolation or extrapolation to generate side-information. However, as motion complexity of the video sequence increases, such generators fail to provide reliable side information. This paper proposes a pixel-domain distributed video coding method, combining low-complexity encoder-side bitplane motion estimation with decoder-side motion-compensated frame interpolation. It is shown that such a system is more suitable for sequences with increased motion complexity, compared to codecs that employ motion estimation at the decoder only.

This paper proposes a novel segmentation-driven direction-adaptive discrete wavelet transform (SD DADWT), wherein the adaptation of the directional wavelet bases is performed on the segments describing the natural geometry of the image. First, a multi-resolution segmentation of the image is performed, obtained through an Edgmentation procedure. The optimum lifting directions are then selected for each segment and at each resolution. The proposed SD DADWT retains the inherent advantages offered by a multiresolution representation of the geometric features in the image, and in the same time provides a sparse image representation via DADWT. Preliminary experimental results obtained in a coding application show that the visual quality of the reconstructed image can be further improved by applying a geometrically-oriented transform on segments that approximate the natural borders in the image.

This paper proposes an optimized joint source-channel coding methodology with unequal error protection for the transmission of video encoded with the recently developed scalable extension of H.264/MPEG-4 AVC. The proposed methodology uses a simplified Viterbi-based search method which significantly outperforms the classical exhaustive search method in terms of computational complexity, leading to a practically applicable solution at the expense of a minimal loss of optimality. Experimental results show the effectiveness of our protection methodology and illustrate its capability to provide graceful degradation in the presence of channel mismatches.

In this paper, we propose a joint source-channel coding (JSCC) methodology which minimizes the end-to-end distortion for the transmission of H.264/MPEG-4 scalable video over packet loss channels. The proposed JSCC-approach employs low-density parity-check codes in order to provide channel protection and relies on Lagrangian-based optimization techniques to derive the appropriate protection levels for each layer produced by the scalable source codec. Experiments show that our JSCC methodology delivers competitive results to state-of-the-art Lagrangian-based algorithms. However, in contrast to the state-of-the-art, our approach significantly reduces the computational complexity. We conclude that the proposed JSCC methodology provides optimized resilience against transmission errors in scalable video streaming over variable-bandwidth error-prone channels.

Unequal error protection (UEP) is the most practical way to achieve joint source-channel coding while keeping a clean separation between the source and channel coders. In this paper, a generic UEP allocation methodology is proposed. The methodology is based on a run-time algorithm, exploiting design-time models, and is applicable to bit-level transmission scenarios as well as packet-based transmission environments. It works with source-independent modeling and a linear complexity with respect to the number of substreams in the source coded stream and the number of protection levels available, which makes it an attractive solution for practical scenarios. We obtain a substantial complexity reduction compared to a classical Lagrangian optimization while keeping optimality in the allocation of the protection levels. Given a typical source and channel granularity, we obtain a factor 6 complexity reduction.

This paper reports an information-theoretic analysis of the inter-scale, inter-orientation and inter-location dependencies that exist between curvelet coefficients. We show that the marginal statistics of these coefficients can be accurately modeled using generalized Gaussian density functions. Though generally decorrelated, we find that curvelets exhibit unusually high dependencies in intra-band local micro-neighborhoods, of a magnitude not found for instance in classical wavelets. Furthermore, dependencies are subject to and decrease with increasing orientation and location differences. Finally, we conclude that intra-band coefficient dependencies are stronger than either their inter-scale or inter-direction counterparts.

This paper describes a surface flattening technique, which has been developed in particular to obtain a complete view of the cortical surface of the brain. However, the method is able to produce an overall planar view of any anatomical or real-life object, provided it is topologically compatible with the sphere (i.e. genus 0). It computes the shading of the original surface for rays casted from a nearby surrounding surface and unfolds this surface in a 2D plane, without introducing major distortions. The flat image consisting of the mapped shading results has the advantage that the sulci (i.e. the grooves characterizing the superficial brain geometry) of the cortical surface of the brain can be followed in their entirety, which facilitates the study and the recognition of their patterns. The new visualization method is integrated into a versatile medical image analysis environment. A first study to assess its usefulness has been accomplished and is also reported in this paper.

Among the different classes of coding techniques proposed in literature, predictive schemes have proven their outstanding performance in near-lossless compression. However, these schemes are incapable of providing embedded L(infinity)-oriented compression, or, at most, provide a very limited number of potential L(infinity) bit-stream truncation points. We propose a new multidimensional wavelet-based L(infinity)-constrained scalable coding framework that generates a fully embedded L(infinity)-oriented bit stream and that retains the coding performance and all the scalability options of state-of-the-art L2-oriented wavelet codecs. Moreover, our codec instantiation of the proposed framework clearly outperforms JPEG2000 in L(infinity) coding sense.

Real-time delivery of video over best-effort error-prone packet networks requires scalable erasure-resilient compression systems in order to 1) meet the users' requirements in terms of quality, resolution, and frame-rate; 2) dynamically adapt the rate to the available channel capacity; and 3) provide robustness to data losses, as retransmission is often impractical. Furthermore, the employed erasure-resilience mechanisms should be scalable in order to adapt the degree of resiliency against transmission errors to the varying channel conditions. Driven by these constraints, we propose in this paper a novel design for scalable erasure-resilient video coding that couples the compression efficiency of the open-loop architecture with the robustness provided by multiple description coding. In our approach, scalability and packet-erasure resilience are jointly provided via embedded multiple description scalar quantization. Furthermore, a novel channel-aware rate-allocation technique is proposed that allows for shaping on-the-fly the output bit rate and the degree of resiliency without resorting to channel coding. As a result, robustness to data losses is traded for better visual quality when transmission occurs over reliable channels, while erasure resilience is introduced when noisy links are involved. Numerical results clearly demonstrate the advantages of the proposed approach over equivalent codec instantiations employing 1) no erasure-resilience mechanisms, 2) erasure-resilience with nonscalable redundancy, or 3) data-partitioning principles.

Due to the improvement of compression technology, limited bandwidth channels can convey more data than before. However, since compressed streams are very sensitive to transmission errors, a joint optimization of the source-channel coding is required. Unequal error protection (UEP) is the most pragmatic way to do it, and exploits the embedded nature of bitstreams produced by scalable coders. However, depending on the number of substreams and protection levels, optimization of UEP can be extremely complex. A large simplification comes from considering the contribution to the global distortion of each substream as independent from the others. This paper explores this distortion additivity assumption for JPEG2000, and shows that though the assumption does not fully hold, no visible consequence on UEP performance is introduced, while reducing significantly the complexity of the optimization process.

Error resilience is of paramount importance in video transmission over variable-bandwidth error-prone channels, such as wireless channels. In this paper, we investigate the influence of corrupted motion vectors in video coding based on motion compensated temporal filtering, and develop various error resilience and concealment mechanisms for this class of codecs. The experimental results show that our proposed motion vector coding technique significantly increases the robustness against transmission errors at the cost of less than 3% in terms of rate. It is also shown that our proposed spatial error-concealment mechanism leads to performance gains of up to 6 dB in comparison to a classical slicing-based approach employing no error concealment.

Real time delivery of video over best-effort networks requires compression systems that dynamically adapt the rate to the available channel capacity and exhibit robustness to data loss as retransmission is often impractical. Error-resilience, however, significantly lowers the coding performance when rigid design is performed based on a worst-case scenario. This paper presents an original scalable video coding scheme that couples the compression efficiency of the open-loop architecture with the robustness of multiple description source coding. The use of embedded multiple description quantization and a novel channel-aware rate-allocation allow for shaping on-the-fly the output bit-rate and the degree of resilience. As a result, robustness to data losses is traded for better visual quality when transmission occurs over reliable channels, while error-resilience is introduced when noisy links are involved. The advantage of our proposal is demonstrated in the context of packet-lossy networks.

Recently proposed scalable wavelet-based video codecs using spatial-domain motion compensated temporal filtering (SDMCTF) offer competitive compression performance when compared to H.264 and generate embedded bit-streams supporting quality, resolution and temporal scalability. To be able to support a large range of bit-rates with optimal compression efficiency, these codecs require a quality-scalable motion vector coding technique. Such an algorithm based on the integer wavelet transform followed by embedded coding of the wavelet coefficients was proposed in the recent past. In this paper, we present a quality-scalable motion vector coding algorithm using median-based motion vector prediction. The compression performance of the proposed algorithm is compared to that of the wavelet-based technique and is found to be superior. Additionally, the proposed motion vector codec is incorporated into an SDMCTF-based video codec and the benefits of using quality-scalable motion vector representations are experimentally demonstrated.

A new family of embedded multiple description scalar quantizers (EMDSQ) that support the progressive transmission of images over variable-bandwidth error-prone channels is proposed in this paper. A control mechanism that allows for tuning the redundancy between the two descriptions for each quantization level is also designed. The employed mechanism enables control of the tradeoff between the coding efficiency and error-resilience, and provides an increased robustness by improving the error resilience in the most important layers of the embedded bit-streams. Instantiations of the proposed family are incorporated in a wavelet-based embedded coding system, and the redundancy-control mechanism is practically demonstrated. Experimental results show that the proposed EMDSQ outperform the state-of-the-art multiple description uniform scalar quantizers (MDUSQ) previously proposed in the literature for error-resilient progressive image transmission.

Video coding techniques yielding state-of-the-art compression performance require large amount of computational resources, hence practical implementations, which target a broad market, often tend to trade-off coding efficiency and flexibility for reduced complexity. Scalable video coding instead, not only provides seamless adaptation to bit-rate variation, but also allows the end user to trim down the resources he needs to perform real-time decoding by limiting the process to a subset of the original content. Hence, by choosing the quality, frame-rate and/or resolution of the reconstructed sequence, each decoder can meet its hardware limitations without affecting the encoding process of the media provider. This paper proposes a preliminary analysis of the memory-access behavior of a fully scalable video decoder and investigates the capability of selecting the operational settings in order to adapt to the available hardware resources on the target device.

In wavelet-based L/sub /spl infin//-constrained embedded coding, the bit-stream is truncated at the bit-rate that corresponds to a guaranteed, user-defined distortion bound. The letter analyzes the optimality of embedded deadzone scalar-quantizers for high-rate L/sub /spl infin//-constrained scalable wavelet-based image coding. A rate-distortion model applicable to the family of embedded deadzone scalar-quantizers is derived and experimentally validated. Conclusions are drawn regarding the optimal subband-quantizer instantiations. The optimal quantizers are employed in a coding algorithm that retains the coding performance and the flexibility options of wavelet-based codecs while allowing for a fully embedded L/sub /spl infin//-oriented bit-stream.

MESHGRID is a novel, compact, multiscalable and animation-friendly surface representation method, which has been introduced in MPEG-4 . The MESHGRID representation attaches a description of the "global connectivity" between the vertices on the object's surface (i.e., the 3-D connectivity wireframe) to a regular 3-D grid of points (i.e., the reference grid). MESHGRID efficiently encodes the 3-D connectivity wireframe by using a new type of 3-D extension of Freeman chain-code. MESHGRID does not explicitly store the polygons of the surface, since the 3-D connectivity wireframe has particular connectivity properties allowing for the unambiguous derivation of the triangulation. The reference grid is a smooth vector field defined on a regular discrete 3-D space. This grid is efficiently compressed by using an embedded 3-D wavelet-based multiresolution intra-band coding algorithm. MESHGRID can be efficiently exploited for QoS since it allows for three types of scalability in both view-dependent and view-independent scenarios, including: 1) resolution scalability, i.e., the adaptation of the number of transmitted vertices; 2) shape precision, i.e., the adaptive reconstruction of the reference grid positions; and 3) vertex position scalability, i.e., the change of the precision of known vertex positions with respect to the reference grid. Furthermore, in addition to the classical vertex-based animation, MESHGRID also supports specific animation capabilities, such as: 1) rippling effects by changing the position of the vertices relative to corresponding reference grid points and 2) reshaping on a hierarchical basis of the regular reference grid and its attached vertices.

Summary form only given. Several methods for L/sub /spl infin//-distortion constrained compression have been proposed that target a set of fixed reconstruction-error bounds. Recently, a wavelet-based L/sub /spl infin//-constrained embedded image-coding technique was proposed that guarantees the required distortion bound while retaining the coding performance and scalability option state-of-the-art wavelet-based L/sub /spl infin//-oriented codecs. The optimality of embedded deadzone scalar-quantizers for high-rate L/sub /spl infin//-constrained scalable wavelet-based coding of images was analyzed. The optimal quantizers were employed in a coding algorithm that outperforms embedded L/sub /spl infin//-oriented wavelet-coders in terms of maximum absolute error. The algorithm was briefly described and coding results were provided.

This paper presents a novel fully-scalable wavelet video coding scheme that performs efficient open-loop motion compensated temporal filtering (MCTF) in the wavelet domain (in-band). Unlike the conventional spatial-domain MCTF (SDMCTF) schemes, which apply MCTF on the original image data and then encode the residual image using a critically-sampled wavelet transform, the framework presented here applies the in-band MCTF (IBMCTF) after the discrete wavelet transform (DWT) is performed in the spatial dimensions. To overcome the inefficiency of motion estimation (ME) in the wavelet domain, a complete-to-overcomplete DWT (CODWT) is performed. The proposed framework provides improved quality (SNR) and temporal scalability as compared with existing in-band closed-loop temporal prediction schemes with ODWT and improved spatial scalability as compared to SDMCTF. We present a thorough comparison between SDMCTF and the proposed IBMCTF in terms of coding efficiency and scalability. Furthermore, we describe several extensions that enable the filtering of the various bands to be performed independently, based on the resolution, sequence content, complexity requirements and desired scalability.

Robust progressive image transmission over unreliable channels with variable bandwidth requires multiple description coding (MDC) systems that produce highly error-resilient embedded bitstreams. The proposed embedded multiple description scalar quantizers (EMDSQ) meet the desired features consisting of a high redundancy level, fine grain rate adaptation and progressive transmission of each description. Experimental results show that EMDSQ yield better rate-distortion performance in comparison to the multiple description uniform scalar quantizers; (MDUSQ) previously proposed in the literature. Moreover, the generalized form of EMDSQ targeting an arbitrary number of channels is proposed, which offers the possibility of designing realistic coders for practical multi-channel communication systems.

It has been demonstrated recently that 3-D wavelet coding with motion-compensated temporal filtering (MCTF) provides a wide range of spatio-temporal-SNR scalability with state-of-the-art coding performance. However, the coding system is very different from the already standardized motion-compensated DCT (MC-DCT) video coders such as MPEG-2, MPEG-4, or H.26L. Nonetheless, the market acceptance of the new scalable technology will come much easier if backward compatibility to such previous standards would exist. In this paper, we present a new coder architecture where the base layer can be a standard MC-DCT coder, while the enhancement layer is an in-band MCTF codec operating in the overcomplete wavelet domain. First, we propose a simple extension of the scalable 3-D wavelet codec with a standard MC-DCT base layer. As a second step, to improve the performance of the proposed scalable coder over a wide range of bit-rates, we describe several small extensions to the standardized MC-DCT codec structure that can be applied for the base layer coding.

Recently, a new wavelet-based video codec using in-band motion compensated temporal filtering (IBMCTF) was introduced This codec is fully scalable in resolution, quality aid frame-rate. In comparison to an equivalent video coding scheme based on spatial domain motion compensated temporal filtering (SDMCTF), its compression performance when decoding to lower resolutions is very promising. However, since the IBMCTF scheme is based on in-band motion estimation, considerably more motion vector data is generated than in the SDMCTF scheme. Efficient compression of these motion vectors is therefore of utmost importance. In this paper, several solutions for the compression of motion vectors generated by a video codec based on IBMCTF are presented and compared.

The paper proposes a new framework for the control of the distortion variation in video coding schemes based on motion-compensated temporal filtering (MCTF). The distortion in an arbitrary decoded frame at any temporal level in the MCTF pyramid is expressed as a function of the distortions in the reference frames at the same temporal level. The approach is formulated for the bi-directional unconstrained MCTF (UMCTF) scheme of Turaga et al., (2002), which does not include the update-lifting step. The proposed framework can be extended to the generalized form of MCTF by utilizing additional control parameters. Experimental results demonstrate the control of the distortion variation in video coding systems based on spatial-domain and wavelet-domain MCTF. One concludes that the proposed framework provides the means of controlling the tradeoff between the average distortion and the distortion variation in each group-of-pictures (GOPs) within the decoded sequence.

Robust progressive image transmission over unreliable channels with variable bandwidth requires multiple description coding (MDC) systems that produce highly error-resilient embedded bit-streams. The proposed embedded multiple description scalar quantizers (EMDSQ) meet the desired features consisting of a high redundancy level, fine grain rate adaptation and progressive transmission of each description. Experimental results show that EMDSQ yield better rate-distortion performance in comparison to the multiple description uniform scalar quantizers (MDUSQ) previously proposed in the literature. Moreover, the generalized form of EMDSQ targeting an arbitrary number of channels is proposed, which offers the possibility of designing realistic coders for practical multi-channel communication systems.

Several techniques based on the three-dimensional (3-D) discrete cosine transform (DCT) have been proposed for volumetric data coding. These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications. This paper gives an overview of several state-of-the-art 3-D wavelet coders that do meet these requirements and proposes new compression methods exploiting the quadtree and block-based coding concepts, layered zero-coding principles, and context-based arithmetic coding. Additionally, a new 3-D DCT-based coding scheme is designed and used for benchmarking. The proposed wavelet-based coding algorithms produce embedded data streams that can be decoded up to the lossless level and support the desired set of functionality constraints. Moreover, objective and subjective quality evaluation on various medical volumetric datasets shows that the proposed algorithms provide competitive lossy and lossless compression results when compared with the state-of-the-art.

In this paper we elaborate on a recently proposed approach for scalable video-coding based on in-band prediction in the overcomplete wavelet domain. It is shown that, through a new calculation scheme for the level-by-level complete to overcomplete discrete wavelet transform (DWT) that exploits certain symmetries, important reductions in the multiplication budget are obtained in comparison to the fastest-known algorithm of the literature. Based on the derived overcomplete transform-domain coefficients, a pixel-accurate motion estimation and compensation (ME/MC) algorithm is proposed, which provides a hybrid-coding framework that supports full scalability in resolution, quality and frame rate. To give an indication of the coding performance of such a system, some preliminary results are reported.

A new in-band motion compensation algorithm for wavelet-based video coding is proposed: the bottom-up prediction algorithm (BUP). The BUP algorithm overcomes the periodic shift-invariance of the discrete wavelet transform (DWT) and is formalized into new prediction rules using filtering operations. BUP is based on the relationships between the subbands of the shifted input signal and the subbands of the non-shifted reference signal, whereby the number of shifts is limited by the periodic shift-invariance of the DWT. We derive the algorithm for the 1-D DWT with three decomposition levels. The combination of all prediction rules of the BUP algorithm defines a new transform: the bottom-up overcomplete DWT or BUP ODWT, which is shift-invariant. The BUP ODWT calculates the overcomplete subbands by applying the prediction rules to the critically sampled subbands of a wavelet-transformed image. The envisaged application for the BUP algorithm is spatially scalable video coding.

This paper assesses the coding performance of several state-of-the-art 3D wavelet coders and compares them with newly proposed compression methods exploiting quadtree and block-based coding concepts, layered zero-coding principles and context-based arithmetic coding. The discussed wavelet-based coding algorithms produce losslessly compressed embedded data streams, supporting the desired progressive data transmission functionality. Moreover, objective and subjective quality evaluations on various medical volumetric datasets show that the newly proposed algorithms provide competitive lossy and lossless compression results compared to the state-of-the-art.

MESHGRID is a novel, compact, multi-scalable and animation-friendly surface representation method, which has been introduced in MPEG-4. The MESHGRID representation attaches a description of the "global connectivity" between the vertices on the object's surface (i.e. the 3D connectivity wireframe) to a regular 3D grid of points (i.e. the reference-grid). The 3D connectivity wireframe is efficiently encoded by using a new type of 3D extension of Freeman chain-code. MESHGRID does not explicitly store the polygons of the surface, since the 3D connectivity wireframe has particular connectivity properties allowing for the unambiguous derivation of the triangulation. The reference-grid is a smooth vector field defined on a regular discrete 3D space. This grid is efficiently compressed by using an embedded 3D wavelet-based multi-resolution intra-band coding algorithm. MESHGRID allows for three types of scalability in both view-dependent and view-independent scenarios: resolution scalability, shape precision, and vertex position scalability. Furthermore, in addition to the classical vertex-based animation, MESHGRID supports specific animation capabilities, such as rippling effects and reshaping on a hierarchical basis of the regular reference-grid and its attached vertices.

This paper describes an efficient implementation of an image coding system based on the independent wavelet-tree coding concept. The system consists of a transform and a (de)coding engine that operate in a pipelined fashion. The main focus of this paper is on the encoding part since, due to the system architecture, the decoder has identical memory utilization. Experimental results prove that the proposed system achieves comparable coding performance to the state-of-the-art, while it localizes the memory accesses to small memory modules and uses minimal computational resources.

A new method for the implementation of the binary-tree decomposition of the convolution-based wavelet transform, called the local wavelet transform (LWT) has been recently proposed in the literature. While it produces exactly the same results as the classical row-column implementation of the transform, it has many implementation benefits. This fact is shown experimentally for the first time for a general-purpose processor-based architecture, by comparing our C implementation of the LWT with an optimal C implementation of the lifting-scheme row-column algorithm. The comparisons are made for the forward multilevel binary-tree decomposition using the 9/7 filter pair, in the typical Intel Pentium processor family.

A new region-based compression scheme for color images is proposed. The segmentation method belongs to the split and merge category. Splitting is carried out using the watershed transform. In the merging stage, a fuzzy color preserving rule-based system is introduced to express the dissimilarity between two regions. The compression part is based on the shape adaptive DCT with /spl Delta/DC correction method. The quantization matrices have been designed according to the properties of the used transform. This method yields promising results, mostly attributed to the efficiency of the segmentation and particularly the fuzzy dissimilarity estimation.

The authors describe a model-based method for the automatic extraction of linear features, like roads and paths, from aerial images. The paper combines and extends two earlier approaches for road detection in SAR satellite images and presents the modifications needed for the application domain of airborne image analysis together with representative results.

We study the compression performance of the quincunx discrete wavelet transform (DWT) and we compare it with the dyadic DWT in terms of rate-distortion. The 2D non-separable quincunx wavelet filters are designed by making use of the transformations of variables technique of Tay and Kingsbury (1993) starting from the 1D biorthogonal (9,7)-taps filters. The applied transformation functions are respectively based on the Kaiser and Chebyshev windows, and on the Lagrange halfband filters. The novelties of this work are in the evaluation of these filters for image compression and the usage of an embedded coder based on the quadtree approach for coding the quincunx subbands.

A major problem in video surveillance is the robust detection of intrusive motion. A widespread technique is frame differencing, initiating an alarm procedure when the difference exceeds a predefined threshold. However, background motion and conflict motion are hard to distinguish. Full-search (FS) block-based motion estimation can be made more robust with respect to the rejection of very small motions, but it is still too sensitive and it has also the disadvantage of the high computational complexity. In our proposal we suggest to use a detection technique that is based on a new center-biased search technique and an original adaptive motion classification system. The proposed approach was validated via an implementation on a very long instruction word (VLIW) processor.

Automatic recovery of eye gestures from image sequences is one of the important topics for face recognition and model-based coding of videophone sequences. Usually, complicated models of the eye and its motion are used. In this paper an eye gesture parameter estimation is described. A previously published automatic eye detection/tracking algorithm, based on template matching, is used for the eye pose detection. The eye gesture analysis is realised with a mathematical morphology scale-space approach, forming spatio-temporal curves out of scale measurement statistics. The resulting curves provide a direct measure of the eye gesture, which can then be used as an eye animation parameter. Experimental results demonstrate the efficiency and robustness of the method.

A number of sub-optimal, but faster, search algorithms have been proposed in the literature, in order to alleviate the complexity associated with motion estimation by the full-search method. A new sub-optimal center-biased adaptive search algorithm for motion estimation is proposed; we refer to this algorithm as center-biased dynamic MInima Bounded Area Search (MIBAS) algorithm. The novelty of MIBAS is the checking point pattern at each subsequent step, composed of points lying in the area bounded by two local minima found at the present step, rather than points lying in a small neighborhood around a local minimum. The simulation results show that, compared to other fast algorithms, the center-biased MIBAS is more robust and produces smaller prediction errors and more reliable motion vectors, while it has a comparable computational complexity.

A minimum-norm least-squares image-reconstruction method for the reconstruction of magnetic resonance images from non-Cartesian sampled data is proposed. The method is based on a general formalism for continuous-to-discrete mapping and pseudoinverse calculation. It does not involve any regridding or interpolation of the data and therefore the methodology differs fundamentally from existing regridding-based methods. Moreover, the method uses a continuous representation of objects in the image domain instead of a discretized representation. Simulations and experiments show the possibilities of the method in both radial and spiral imaging. Simulations revealed that minimum-norm least-squares image reconstruction can result in a drastic decrease of artifacts compared with regridding-based reconstruction. Besides, both in vivo and phantom experiments showed that minimum-norm least-squares image reconstruction leads to contrast improvement and increased signal-to-noise ratio compared with image reconstruction based on regridding. As an appendix, an analytical calculation of the raw data corresponding to the well-known Shepp and Logan software head phantom is presented.

Perfect reconstruction, quality scalability, and region-of-interest coding are basic features needed for the image compression schemes used in telemedicine applications. This paper proposes a new wavelet-based embedded compression technique that efficiently exploits the intraband dependencies and uses a quadtree-based approach to encode the significance maps. The algorithm produces a losslessly compressed embedded data stream, supports quality scalability, and permits region-of-interest coding. Moreover, experimental results obtained on various images show that the proposed algorithm provides competitive lossless/lossy compression results. The proposed technique is well suited for telemedicine applications that require fast interactive handling of large image sets, over networks with limited and/or variable bandwidth.

The final diagnosis in coronary angiography has to be performed on a large set of original images. Therefore, lossless compression schemes play a key role in medical database management and telediagnosis applications. This paper proposes a wavelet-based compression scheme that is able to operate in the lossless mode. The quantization module implements a new way of coding of the wavelet coefficients that is more effective than the classical zerotree coding. The experimental results obtained on a set of 20 angiograms show that the algorithm outperforms the embedded zerotree coder, combined with the integer wavelet transform, by 0.38 bpp, the set partitioning coder by 0.21 bpp, and the lossless JPEG coder by 0.71 bpp. The scheme is a good candidate for radiological applications such as teleradiology and picture archiving and communications systems (PACS's).

A folded very large scale integration (VLSI) architecture is presented for the implementation of the two-dimensional discrete wavelet transform, without constraints on the choice of the wavelet-filter bank. The proposed architecture is dedicated to flexible block-oriented image processing, such as adaptive vector quantization used in wavelet image coding. We show that reading the image along a two-dimensional (2-D) pseudo-fractal scan creates a very modular and regular data flow and, therefore, considerably reduces the folding complexity and memory requirements for VLSI implementation. This leads to significant area savings for on-chip storage (up to a factor of two) and reduces the power consumption. Furthermore, data scheduling and memory management remain very simple. The end result is an efficient VLSI implementation with a reduced area cost compared to the conventional approaches, reading the input data line by line.

This work proposes a new block based motion estimation and compensation technique applied on the detail images of the wavelet pyramidal decomposition. The algorithm uses two matching criteria, namely the absolute difference and the absolute sum. For a wavelet decomposed one-dimensional step function, it is shown that for odd translations of the step, the absolute sum reaches a smaller minimum than the absolute difference. We also derive in this case a constraint on the highpass filter coefficients so that a zero prediction error can be reached by using the absolute sum. Although this cannot be easily generalized for an arbitrary signal profile, experimental results obtained with photorealistic image sequences indicate that the prediction error can be reduced with respect to techniques that only use the absolute difference as matching criterion.

In this paper a segmented video codec with hybrid motion estimation and inherent bit rate control capability is presented. The first frame in the data is always encoded in intraframe mode, while the rest of the frames are encoded in interframe mode. The interframe encoding is based on (1) hybrid conventional/perspective block motion vector estimation and coding, and (2) coding of the prediction error (displaced frame difference, "DFD") using segmented image coding techniques. We present a way to partition the DFD in moving and static regions and we explain how this classification strategy can be used as a means to control the bit rate. The simulation results show that the hybrid motion estimation technique outperforms the conventional full search block-matching method by improving the overall PSNR for reduced bit rate, and that the bit rate control strategy, although simple, is very efficient for monitoring both the bit rate and the quality degradation.

Extraction of stationary objects, including the background, and restriction of the coding to moving objects, has been widely proposed in the literature as a means of achieving very low bit rate transmission, while ensuring high image quality. In cases where it is not feasible to code and transmit all the objects identified as moving, due to bandwidth limitations, we should ensure that at least the most visually important objects (i.e. the ones that contribute most to the quality improvement) are coded. In this paper, different methods for segmenting image sequences and obtaining the moving objects are investigated. For each segmentation method, we identify a hierarchical ranking of the moving objects, in terms of visual importance. We study the influence of the segmentation algorithm's choice on the performance of a region-based video coding scheme, and we present simulation results that demonstrate the efficiency of the criteria proposed for hierarchical moving object coding.

Signal compression can be obtained by wavelet transformation of integer input data followed by quantification and coding. As the quantification is usually lossy, the whole compression/decompression scheme is lossy too. We define a critical wavelet coefficient quantification, i.e., the coarsest quantification that allows perfect reconstruction. This is demonstrated for the Haar transform and for arbitrarily smooth wavelet transforms derived from it. The new integer wavelet transform allows implementation of multiresolution subband compression schemes, in which the decompressed data are gradually refined, retaining the option of perfect reconstruction.

A segmented image coding algorithm for video sequences is presented. The first frame in the data is always encoded in an intraframe mode, while the rest of the frames in the data are encoded in an interframe mode. The interframe encoding is based on (1) block motion vector estimation and coding, (2) segmentation of the prediction error image and classification of the regions in the foreground/background, (3) contour simplification and coding, and (4) texture approximation by a linear combination of weakly separable base functions and coefficient coding. The contour simplification leads to an average reduction of 30% in the number of bits needed for the contour coding, the system can be adjusted at different bit-rates by parameter tuning, the simulation results are of high quality in terms of PSNR and they show that our coding approach is particularly promising for very low bit-rate applications.

Describes a knowledge-based image interpretation system for the segmentation and labeling of a series of 2-D brain X-ray CT-scans, parallel to the orbito-meatal plane. The system combines the image primitive information produced by different low level vision techniques in order to improve the reliability of the segmentation and the image interpretation. It is implemented in a blackboard environment that is holding various types of prior information and which controls the interpretation process. The scoring model is applied for the fusion of information derived from three types of image primitives (points, edges, and regions). A model, containing both analogical and propositional knowledge on the brain objects, is used to direct the interpretation process. The linguistic variables, introduced to describe the propositional features of the brain model, are defined by fuzzy membership functions. Constraint functions are applied to evaluate the plausibility of the mapping between image primitives and brain model data objects. Procedural knowledge has been integrated into different knowledge sources. Experimental results illustrate the reliability and robustness of the system against small variations in slice orientation and interpatient variability in the images.

The Karhunen-Loeve transform (KLT) is applied to the analysis of dynamic sequences of thermograms describing the temporal evolution of body surface temperature following the application of an external thermal stimulus. The KLT may be evaluated either along the spatial or temporal dimensions of the data; the duality of both representations is emphasized. An example is presented to illustrate that the KLT allows an efficient data reduction and facilities tumor detection by highlighting physiologically important abnormalities in the time behavior of thermal patterns.>