Preparing subjective evaluations for point cloud quality assessment using 2D or 3D planar displays typically requires rendering uncompressed videos, which demands large volumes of data storage and processing, particularly at high resolutions. This paper describes a subjective quality evaluation of point clouds using a head-mounted display (HMD), which allows the direct 3D content depiction and thus eliminates the need for video rendering. The test dataset included six static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the learning-based codec RS-DLPCC, and the Draco codec. The obtained Mean Opinion Scores (MOS) were compared with two other studies using the same dataset but with different displays, notably a 2D display and a 3D stereoscopic display. The reported results indicate that the three studies are highly correlated, with Pearson and Spearman correlation coefficients above 0.9. Moreover, a multi-way ANOVA shows that changing the display does not have a significant effect on the obtained MOS.

A study on the quality evaluation of point clouds in the presence of coding distortions is presented. For that, four different point cloud coding solutions, notably the standardized MPEG codecs G-PCC and V-PCC, a deep learning-based coding solution RS-DLPCC, and Draco, are compared using a subjective evaluation methodology. Furthermore, several full-reference, reduced-reference and no-reference point cloud quality metrics are evaluated. Two different point cloud normal computation methods were tested for the metrics that rely on them, notably the Cloud Compare quadric fitting method with radius of five, ten, and twenty and Meshlab KNN with K six, ten, and eighteen. To generalize the results, the objective quality metrics were also benchmarked on a public database, with mean opinion scores available. To evaluate the statistical differences between the metrics, the Krasula method was employed. The Point Cloud Quality Metric reveals the best performance and a very good representation of the subjective results, as well as being the metric with the most statistically significant results. It was also revealed that the Cloud Compare quadric fitting method with radius 10 and 20 produced the most reliable normals for the metrics dependent on them. Finally, the study revealed that the most commonly used metrics fail to accurately predict the compression quality when artifacts generated by deep learning methods are present.

In this paper, a stability analysis of the JPEG Pleno Learning-based Point Cloud Coding Verification Model (VmUC) is performed. The codec is a deep learning-based solution that is able to compress both color and geometry. Three different training sessions were conducted using the default training set and cost function, and six point clouds were encoded/decoded with the resulting operating points for six target distortion/bitrate ratios. The VmUC performance was compared with the MPEG codecs V-PCC and G-PCC, considering three objective metrics, notably PSNR MSE D1, PSNR MSE D2, and PCQM. PSNR MSE D1 was also computed at each training epoch for the six decoded point clouds. It is concluded that the VmUC is able to outperform G-PCC and V-PCC in geometry encoding. However, it is outperformed by V-PCC in terms of color encoding, namely across all three training sessions. Furthermore, it is also shown that the codec does not present a high level of stability, changing its performance considerably with different training sessions.

In this paper, a subjective evaluation of static point clouds encoded with several codecs is described. Unlike other studies, a stereoscopic 3D display was used to visualize the 3D representation. A set of six point clouds were encoded using a set of state of the art point cloud coding solutions, notably the two MPEG codecs V-PCC and G-PCC, a deep learning solution RS-DLPCC that was the response to a call for evidence on point cloud coding of JPEG Pleno, and the popular DRACO codec. The results of this subjective quality evaluation using a 3D representation visualized in a stereoscopic display were compared with a previous subjective study that used the same content visualized in a 2D display. For that, the results of both tests were compared with the Pearson correlation, Spearman rank order correlation, the root mean square error and the outlier ratio. Moreover, the two subjective evaluation results were statistically analysed to seek for any statistical difference. The two subjective evaluations reveal a very high level of similarity.

Recently, a larger interest in the different plenoptic formats, including digital holograms, has emerged. Aside from other challenges that several steps of the holographic pipeline, from digital acquisition to display, have to face, visual quality assessment of compressed holograms is particularly demanding due to the distinct nature of this 3D image modality when compared to regular 2D imaging. There are few studies on holographic data quality assessment, particularly with respect to perceptual effects of lossy compression. This work aims to study the quality evaluation of digital hologram reconstructions, presented on regular 2D displays, in the presence of compression distortions. As there is no established or generally agreed compression methodology for digital holograms compression on the hologram plane with available implementations, a set of state-of-the-art compression codecs, namely HEVC, AV1, and JPEG2000, were used for compression of the digital holograms on the object plane. Both computer generated and optically generated holograms were considered. Two subjective tests were conducted to evaluate distortions caused by compression. The first subjective test was conducted on the reconstructed amplitude images of central views, while the second test was conducted on pseudo-videos generated from the reconstructed amplitudes of different views. The subjective quality assessment was based on mean opinion scores. A selection of objective quality metrics was evaluated, and their correlations with mean opinion scores were computed. The VIFp metrics appeared to have the highest correlation.

In light field image compression, facilitating random access to individual views plays a significant role in decoding views quickly, reducing memory footprint, and decreasing the bandwidth requirement for transmission. Highly efficient light field image compression methods mainly use inter view prediction. Therefore, they typically do not provide random access to individual views. On the other hand, methods that provide full random access usually reduce compression efficiency. To address this trade-off, a light field image encoding method that favors random access is proposed in this paper. Light field image views are grouped into independent (3× 3) views, which are called Macro View Images (MVIs) . To encode MVIs, the central view is used as a reference to compress its adjacent neighboring views using a hierarchical reference structure. To encode the central view of each MVI, the most central view along with the center of a maximum of three MVIs, are used as reference images for the disparity estimation. In addition, the proposed method allows the use of parallel processing to reduce the maximum encoding/decoding time-complexity in multi-core processors. Tile partitioning can also be used to randomly access different regions of the light field images. The simulation results show that the proposed method outperforms other state-of-the-art methods in terms of compression efficiency while providing random access to both views and regions of interest.

In preparation of the Call for Proposals on JPEG Pleno Holography, multiple exploration studies are ongoing to define the general procedure for performance assessment of proposed coding solutions. The performance of proposals will be compared against JPEG 2000 and H.265/HEVC intra coding, both serving as anchor codecs. In this paper, we report on the results of a dynamic subjective visual quality assessment procedure. A subset of 8 holograms, selected from the JPEG Pleno Database, is compressed both in hologram and object plane at three different bit rates. Chosen bit rates vary per hologram depending on their content characteristics. Then, for each hologram and its compressed versions, pseudo-video sequences are generated from the reconstructed views along a scan path that involves focus and viewing angle changes and that is hologram specific. A double stimulus simultaneous test, combined with a 5-level impairment-scale scoring protocol is deployed where videos created from the reference and the decoded holograms are visualized side by side on a professional 4K display. Results demonstrate that this test procedure requires deep scenes and sufficient scene complexity throughout the depth stack to allow for adequate stress testing of the codecs under test, particularly those solutions that adhere to compression in the object plane.

Recently, there has been a growing interest in the various formats of plenoptic information, including digital holograms. However, the reduced number of studies on holographic data quality assessment results in a difficult analysis of the effects caused by any type of post-processing procedures, like lossy compression. In this work, several state of the art compression codecs, namely HEVC, AV1, and JPEG2000, are applied to the digital holographic data complex representation on the reconstruction plane. Both computer generated and experimental holograms are considered. The perceptual quality of the amplitude is evaluated to measure the quality degradation at four different bit rates. The performance of a selection of quality metrics is also tested.

This paper presents a quality evaluation study of point cloud codecs that have been recently standardised by the MPEG committee. In particular, a subjective experiment to assess their performance in terms of bitrate against visual quality is designed and realized in four independent laboratories. The experimental setup of each laboratory varies; yet, the obtained subjective scores exhibit high inter laboratory correlation, confirming that the adopted assessment protocol is robust to equipment selection and viewing conditions, ensuring reliability and facilitating repeatability. Our study confirms the superior compression performance of the MPEG V-PCC, when compared to MPEG G-PCC, in the case of static contents. Finally, results from a benchmark of the most popular objective quality metrics using the obtained subjective scores as ground truth, reveal that the point2plane with mean square error is the most accurate quality predictor, closely followed by the point2point also using mean square error as distance measure.

Pseudo-sequence based light field compression methods are a highly efficient solution to compress light field images. They use state-of-the-art video encoders like HEVC to encode the image views. HEVC exploits Coding Tree Unit (CTU) structure which is flexible and highly efficient but it is computationally demanding. Each CTU is examined in various depths, prediction and transformation modes to find an optimal coding structure. Efficiently predicting depth of the coding units can reduce complexity significantly. In this paper, a new depth decision method is introduced which exploits the minimum and maximum of previously encoded co-located coding units in spatially closer reference images. Minimum and maximum depths of these co-located CTUs are computed for each coding unit and are used to limit the depth of the current coding unit. Experimental results show up to 55% and 85% encoding time reduction with serial and parallel processing respectively, at negligible degradations.

In light field compression, besides coding efficiency, providing random access to individual views is also a very significant factor. Highly efficient compression methods usually lack random access. Similarly, random access methods usually reduce the compression efficiency. To address this trade-off, a light field image encoding method is proposed in this paper which favors random access. In the proposed scheme 15×15 view images are divided into 25 independent 3×3 view images which are called Macro View Image (MVI). To encode MVIs, the central view image is used to compress its immediate neighboring view images using a hierarchical reference structure. To encode the central view of each MVI, the most central view image, along with the center of at most three MVIs, are used as the reference images for the disparity estimation. In addition, the proposed method enables the use of parallel computation to improve encoding/decoding time complexity. To reduce memory footprint in case a Region of Interest (ROI) is required, HEVC tile partitioning is used.

In previous work, a digital hologram compression scheme for representation on the object plane was proposed. Compression on object plane for experimental holograms and Computer-Generated Holograms (CGH) proves to be a very efficient model that outperforms the compression on the hologram plane. However, the compression gain is more relevant in CGHs. The difference between experimental holograms and CGHs is related to the fact that CGHs are less affected by speckle noise that is a characteristic of experimental holograms. In the current work, to improve the coding efficiency of the hologram compression scheme is proposed the reduction of speckle noise of experimental holograms. The compression scheme defines a base layer where a 2D version of the object is coded with an image codec standard. The efficiency of this step is much higher in case of CGH when compared to experimental holograms. However, after performing speckle noise reduction before any compression a similar compression efficiency is found. Since the speckle noise reduction is performed only on amplitude data without affecting the phase, is still possible to render 3D features such as depth map, multi-view or to recover holographic interference patterns for further 3D visualization.

Light field imaging is a promising technology for 3D computational photography. As Light Field images are represented for multiple views, their subjective evaluation is a very demanding task. Hence, identifying reliable objective quality assessment methodologies plays a very important role. In this paper six objective quality metrics; PSNR-Y, PSNR-YUV, SSIM-Y, MSSSIM-Y, FSIM-Y and HDRVDP2-Y are assessed for five state-of-the-art codecs at various bit-rates. Moreover, the metrics are computed in the linear, perceptually uniform and perceptual quantizer spaces. The results are compared against those of a subjective study and is concluded that the average FSIM-Y is the most reliable metric. The paper also introduces maps of the objective metrics to evaluate the quality dispersion among the different light field image views.

Recently stakeholders in the area of multimedia representation and transmission have been looking at plenoptic technologies to improve immersive experience. Among these technologies, point clouds denote a volumetric information representation format with important applications in the entertainment, automotive and geographical mapping industries. There is some consensus that state-of-the-art solutions for efficient storage and communication of point clouds are far from satisfactory. This paper describes a study on point cloud quality evaluation, conducted in the context of JPEG Pleno to help define the test conditions of future compression proposals. A heterogeneous set of static point clouds in terms of number of points, geometric structure and represented scenarios were selected and compressed using octree-pruning and a projection-based method, with three different levels of degradation. The models were comprised of both geometrical and color information and were displayed using point sizes large enough to ensure observation of watertight surfaces. The stimuli under assessment were presented to the observers on 2D displays as animations, after defining suitable camera paths to enable visualization of the models in their entirety and realistic consumption. The experiments were carried out in three different laboratories and the subjective scores were used in a series of correlation studies to benchmark objective quality metrics and assess inter-laboratory consistency.

The perceived image quality of a digital hologram of macroscopic objects is affected by its reduced depth of focus and by speckle noise due to coherent illumination. Several filtering techniques have been proposed for speckle noise reduction of digital holograms but there are scarce quality assessment studies regarding phase-shifting digital holograms. Typically, the performance of these filters on experimental holograms is assessed using no reference objective metrics. However, these metrics do not reflect the subjective visual quality perceived by a human observer. In this work, the performance of four speckle reduction algorithms, namely the non-local means, the Lee, the Frost and the block matching 3D filters, using five different parametrizations for each case, are subjectively compared. Due to a large amount of testing data, the subjective test was divided in two: 1) one phase where the effects of the parametrization of each method are evaluated, 2) a second phase where the best parametrization results are compared between them. The results are ranked with respect to the perceived image quality to obtain the Mean Opinion Scores for each filter/parameter combination. As in this case there is no reference image and double stimulus is desirable, the subjective evaluation was achieved using full paired comparison. The experiment indicates that BM3D and Lee are the preferred filters, and reveals a strong dependence of filter performance on hologram characteristics.

Light fields capture a large number of samples of light rays in both intensity and direction terms, which allow post-processing applications such as refocusing, shifting view-point and depth estimation. However, they are represented by huge amount of data and require a high-efficient coding scheme for its compression. In this paper, light field raw image data is decomposed into multi-views and used as a pseudo-sequence input for state-of-the-art codecs such as High Efficiency Video Coding (HEVC). In order to better exploit redundancy between neighboring views and decrease distances between current view and its references instead of using conventional orders, views are divided into four smaller regions and each region is scanned by a snake order. Furthermore, according to this ordering, an appropriate referencing structure is defined that only selects adjacent views as references. Simulation results show that Rate-Distortion performance of proposed method has higher gain than the other state-of-the-art light field compression methods.

Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octree-based compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective Quality assessment tools.

This paper studies the influence of JPEG-XT on LDR generation using TMOs'. JPEG-XT encodes HDR images into a two layer scheme, encoding a LDR version of the image in a base layer, and the residual HDR information in an enhancement layer. The question addressed here is to understand if this model allows to extract a new LDR representation using a different TMO, independently of the TMO used to generate the LDR image encoded in the base layer. In particular, the use of the Drago, Mai, Mantiuk, Reinhard, and Reinhard & Devlin TMOs' will be considered together with the three main profiles defined on the JPEG-XT standard. Hence, images generate with a TMO applied to the decoded HDR image are compared with the generated from the original HDR image using the popular FSIM metric. Moreover, in cases with similar perceptual quality, the bit rate increase, compared with the JPEG encoding is also studied for the three JPEG-XT profiles.

The rapid adoption of MPEG-DASH is testament to its core design principles that enable the client to make the informed decision relating to media encoding representations, based on network conditions, device type and preferences. Typically, the focus has mostly been on the different video quality representations rather than audio. However, for device types with small screens, the relative bandwidth budget difference allocated to the two streams may not be that large. This is especially the case if high quality audio is used, and in this scenario, we argue that increased focus should be given to the bit rate representations for audio. Arising from this, we have designed and implemented a subjective experiment to evaluate and analyses the possible effect of using different audio quality levels. In particular, we investigate the possibility of providing reduced audio quality so as to free up bandwidth for video under certain conditions. Thus, the experiment was implemented for live music concert scenarios transmitted over mobile networks, and we suggest that the results will be of significant interest to DASH content creators when considering bandwidth tradeoff between audio and video.

The influence of chromatic impairments on the perceived image quality is studied in this article. Under the D65 standard illuminant, a set of hyperspectral images were represented into the CIELAB color space, and the corresponding chromatic coordinates were subdivided into clusters with the k -means algorithm. Each color cluster was shifted by a predefined chromatic impairment Δ E * ab with random direction in a * b * chromatic coordinates only. Applying impairments of 3, 6, 9, 12, and 15 in a * b * coordinates to five hyperspectral images a set of modified images was generated. Those images were shown to subjects that were asked to rank their quality based on their naturalness. The Mean Opinion Score of the subjective evaluations was computed to quantify the sensitivity to the chromatic variations. This article is also complemented with an objective evaluation of the quality using several state-of-the-art metrics and using the CIEDE2000 color difference among others. Analyzing the correlations between subjective and objective quality evaluation helps us to conclude that the proposed quality estimators based on the CIEDE2000 provide the best representation. Moreover, it was concluded that the established quality metrics only become reliable by averaging their results on each color component.

This paper provides a study on the ability of Full-Reference quality metrics to measure the Quality of Experience that results on images that suffer chromatic errors. Considering this, the well known PSNR, MSSIM and VIFP will be compared with the MOS results. It is important to highlight that these measures are usually applied to the image luminance channel. However, in this study only the chromatic components of the image represented in the CIELAB color space have been changed resulting in similar values of luminosity. Moreover, the CIEDE2000 was also considered, because the knowledge of the scene illuminant allows a reliable computation. Using correlation estimates it was concluded that the CIEDE2000 and the MSSIM provides the best MOS estimation. The MSSIM results in a slightly better Pearson correlation value while the CIEDE2000 results in a better RMSE and Outlier Ratio. The VIFP also provides a good correlation, while the PSNR shows a poor correlation with the MOS values.

Dynamic contrast-enhanced magnetic resonance (DCE-MR) of the breast is especially robust for the diagnosis of cancer in high-risk women due to its high sensitivity. Its specificity may be, however, compromised since several benign masses take up contrast agent as malignant lesions do. In this paper, we propose a novel method of 3D multifractal analysis to characterize the spatial complexity (spatial arrangement of texture) of breast tumors at multiple scales. Self-similar properties are extracted from the estimation of the multifractal scaling exponent for each clinical case, using lacunarity as the multifractal measure. These properties include several descriptors of the multifractal spectra reflecting the morphology and internal spatial structure of the enhanced lesions relatively to normal tissue. The results suggest that the combined multifractal characteristics can be effective to distinguish benign and malignant findings, judged by the performance of the support vector machine classification method evaluated by receiver operating characteristics with an area under the curve of 0.96. In addition, this paper confirms the presence of multifractality in DCE-MR volumes of the breast, whereby multiple degrees of self-similarity prevail at multiple scales. The proposed feature extraction and classification method have the potential to complement the interpretation of the radiologists and supply a computer-aided diagnosis system.

Voice over Internet Protocol (VoIP) applications based on peer-to-peer (P2P) communications have been experiencing considerable growth in terms of number of users. To overcome filtering policies or protect the privacy of their users, most of these applications implement mechanisms such as protocol obfuscation or payload encryption that avoid the inspection of their traffic, making it difficult to identify its nature. The incapacity to determine the application that is responsible for a certain flow raises challenges for the effective management of the network. In this paper, a new method for the identification of VoIP sessions is presented. The proposed mechanism classifies the flows, in real-time, based on the speech codec used in the session. To make the classification lightweight, the behavioral signatures for each analyzed codec were created using only the lengths of the packets. Unlike most previous approaches, the classifier does not use the lengths of the packets individually. Instead, it explores their level of heterogeneity in real time, using entropy to emphasize such feature. The results of the performance evaluation show that the proposed method is able to identify VoIP sessions accurately and simultaneously recognize the used speech codec.

A study on the perceived quality of images displayed with color changes is presented. Under the D65 standard illuminant colors are changed in the CIE 1976 (L*a*b*) color space, with the application of a predefined chromatic error ΔE*ab. The colors were initially divided into clusters with the K-Means algorithm. Each color cluster is shifted by the predefined chromatic error with a random direction in a*b* chromatic coordinates. Applying the ΔE*ab errors of 3, 6, 9, 12 and 15 units to the five hyperspectral images a set of modified images was collected. Those images were shown to individuals, that were asked to rank those images quality based on their naturalness. The Medium Opinion Scores was computed and allowed to test and quantify the sensibility to color changes.

The inclusion of encryption or evasive techniques in popular applications increased the importance of characterizing network traffic based on behavior. This study aims to characterize peer-to-peer (P2P) traffic from the perspective of host computers by focusing on the packet lengths. The article explores the dissimilarities between the lengths of Internet Protocol (IP) packets generated by P2P and non-P2P applications. The heterogeneity of those lengths was assessed using entropy and compared for different classes of applications, through the implementation of a sliding analysis window. Initial observations show that the lengths of the packets generated by P2P applications are more varied than those of non-P2P applications. These patterns were used to implement a method to identify hosts running P2P applications. Unlike previous studies on this area, we used the heterogeneity of the packet lengths instead of the length value per se, and a sliding window calculation procedure was adopted to allow real-time processing. The results of this study can be used for the characterization of traffic generated by P2P applications, as well as for traffic classification and management purposes.

doi: 10.1093/comjnl/bxr127 Comp J 2012;55(6): 740–755 In the above article, on page 751, the image used for Figure 7 was incorrect. The image should have been:

The motion compensation is one of the most important mechanisms in the context of video coding, allowing the efficient coding of temporal information. Video coding standards have been using entropy coding for motion information which is more prone to transmission errors than fixed-length coding. In this paper, we propose a joint source-channel decoding (JSCD) scheme for robustly decoding motion vectors, enabling the traditional usage of motion compensation in noisy channel scenarios while still maintaining a good coding efficiency. At the encoder, motion vectors are simply coded using scalar quantization enabling scalable video coding setups. The decoder then tries to recover corrupted motion vectors by means of a maximum-a-posteriori (MAP) estimation. This scheme works in open-loop mode, not requiring feedback from the decoder, which is equally important for noisy channel environments. Because some of the coding complexity is brought from the encoder to the decoder, this scheme can be employed in nontraditional applications like video streaming from low power mobile peers.

Processing medical data has always been an interesting field that has shown the need for effective image segmentation methods. Modern medical image segmentation solutions are focused on 3D image volumes, which originate at advanced acquisition devices. Operating on such data in a 3D environment is essential in order to take the full advantage of the available information. In this paper we present an extended version of our 3D image segmentation and reconstruction model that belongs to the family of Deformable Models and is capable of processing large image volumes in competitive times and in fully 3D environment, offering a big level of automation of the process and a high precision of results. It is also capable of handling topology changes and offers a very good scalability on multi-processing unit architectures. We present a description of the model and show its capabilities in the field of medical image processing.

The number and popularity of applications developed over the Peer-to-Peer (P2P) network paradigm has been growing over the last decade, some of which are dedicated to streaming multimedia content. To deceive traffic shaping mechanisms or improve the security of the communications, these applications generate encrypted traffic or resort to several obfuscation techniques, making it difficult to manage this kind of traffic at the network level. In this work, we propose a method that explores transmission vulnerabilities of the encrypted traffic allowing its detection. Hence, an experimental test bed was created to capture a diversity of traffic, which includes flows of a widely used P2P media streaming application called Goal Bit. The collected traces of traffic were then analysed, and a set of rules was created for the SNORT network intrusion detection system, which allows the successful detection of the encrypted traffic generated by Goal Bit. The accuracy of this system was then validated experimentally.

Wireless channels are more prone to transmission errors than wired communications counterparts, leading to excessive packet retransmission that is very inconvenient on media streaming. The multiple-description-coding (MDC) has proven to be very effective against transmission errors, thus providing a solution for that problem. What is presented is a method for MDC using the highly optimized and scalable JPEG 2000. Descriptions are encoded by our modified version of JPEG 2000 which produces compatible codestreams provided with key-error detection registers. The multiple-description JPEG 2000 decoder is then capable of precisely detecting transmission errors and to efficiently choose between available description-information, achieved by a clever exploitation of the EBCOT system. To test the potential of the proposed method, it is integrated as a spatial MD-Coder in a state-of-the-art joint-source channel video coder framework capable of an efficient bit-allocation between descriptions. A comprehensive set of experimental results is presented.

Traffic monitoring and analysis is of critical importance for managing and designing modern computer networks, and constitutes nowadays a very active research field. In most of their studies, researchers use techniques and tools that follow a statistical approach to obtain a deeper knowledge about the traffic behaviour. Network administrators also find great value in statistical analysis tools. Many of those tools return similar metrics calculated for common properties of network packets. This paper presents NetOdyssey, a new tool for the statistical analysis of network traffic. It is able to capture the traffic from the network card or from a pre-collected trace, and generates several statistics calculated for different traffic properties in separate threads. Its modular architecture allows one to adapt NetOdyssey to specific analysis purposes, resorting to the possibility to easily implement custom-made modules, whose implementation principles are also described in here.

In this paper we present a novel approach for image segmentation using Active Nets and Active Volumes. Those solutions are based on the Deformable Models, with slight difference in the method for describing the shapes of interests - instead of using a contour or a surface they represented the segmented objects with a mesh structure, which allows to describe not only the surface of the objects but also to model their interiors. This is obtained by dividing the nodes of the mesh in two categories, namely internal and external ones, which will be responsible for two different tasks. In our new approach we propose to negate this separation and use only one type of nodes. Using that assumption we manage to significantly shorten the time of segmentation while maintaining its quality.

This paper presents a novel model for 3D image segmentation and reconstruction. It has been designed with the aim to be implemented over a computer cluster or a multi-core platform. The required features include a nearly absolute independence between the processes participating in the segmentation task and providing amount of work as equal as possible for all the participants. As a result, it is avoid many drawbacks often encountered when performing a parallelization of an algorithm that was constructed to operate in a sequential manner. Furthermore, the proposed algorithm based on the new segmentation model is efficient and shows a very good, nearly linear performance growth along with the growing number of processing units.

Deformable models are a popular family of image segmentation techniques, which has been gaining significant focus in the last two decades, serving both for real-world applications as well as the base for research work. One of the features that the deformable models offer and that is considered a much desired one, is the ability to change their topology during the segmentation process. Using this characteristic it is possible to perform segmentation of objects with discontinuities in their bodies or to detect an undefined number of objects in the scene. In this paper we present our model for handling the topology changes in image segmentation methods based on the Active Volumes solution. The said model is capable of performing the changes in the structure of objects while the segmentation progresses, what makes it efficient and suitable for implementations over powerful execution environment, like multi-core architectures or computer clusters.

The increasing number of applications offering their services over peer-to-peer (P2P) platforms is changing the properties of the traffic within computer networks. Their massive use raises a few imperative challenges for network administrators and Internet service providers, regarding the quality of service and security of their networks. It such scenario, it is important to develop mechanisms to control and efficiently manage the P2P traffic and prepare the networks to support it, for which it is necessary to study the effect of P2P applications in the traffic of computer networks and to develop methodologies to characterise its behaviour. In this paper, the characteristics of the traffic generated by P2P applications are analysed from the behavioural point of view, and entropy is used to measure the heterogeneity embedded in the packet sizes. The results obtained show evident difference between P2P and non-P2P traffic, being the proposed approach applicable to real-time and high-speed networks with encrypted P2P traffic, where the existing methodologies are useless.

Summary form only given. The problem of efficient image/video transmission over wireless channels is considered. Such a problem involves good compression rates and effectiveness in presence of channel failures. The proposed work includes a balanced multiple description-coding (MDC) schemes for still image based on the discrete wavelet transform (DWT) developed. This MDC includes an efficient bit allocation procedure that dispatches the source image redundancy between different channels. Extended version for video was presented, and uses the 3D scan-based DWT that involves scan-based MDC with rate or quality control. To solve this problem, redundancy between the descriptors in function of channel model and state (BER) is proposed.

We present a system for video streaming well adapted to the unpredictable and varying nature of Internet. The proposed system uses a superposition of several multiple description coding (MDC) schemes, each with N = 2 descriptions, to reach rate scalability and adaptability to varying channel conditions. Each MDC (N = 2 descriptions), that we will call base MDC has a bit rate and a redundancy associated. The superposition of several base MDC results on a MDC scheme for N > 2 descriptions with different bit rates, allowing rate scalability, for different redundancies. The proposed scheme is well adapted to varying channel conditions. In the proposed method multiple descriptions are generated by the coder and downloaded in the server, leaving to the server the only task to choose sending out the right description at the right time depending of channel conditions (bandwidth and loss rate).

A challenge of image communication over unreliable channels is to achieve good compression rates and be effective in presence of channel failures. In this work we use the multiple description coding (MDC) techniques, based on wavelet transforms, that have been shown to be powerful against channel failures. We propose a bit allocation procedure that dispatch redundancy between the different channels when compressing to a target bit rate with a bounded side distortion. In this way we develop a MDC scheme well adapted to channel noise.

In Pereira et al. (2002) we proposed a balanced multiple description coding (MDC) scheme based on the discrete wavelet transform (DWT). This MDC includes an efficient bit allocation procedure that dispatches the source image redundancy between different channels. The amount of redundancy is controlled according to the bit error rate of the different channels. Here, we propose to extend this approach for low bit rate video transmission. The proposed method uses the 3D scan-based DWT of Parisot et al. (2000) and involves scan-based MDC with rate or quality control.

This paper describes a two-stage digital video encoding technique that is compliant with emerging international standards and is optimized for multiple uses of the material. Applications include production, post-processing, archival storage, and distribution through diverse channels. The first phase of the encoding is computationally intensive and the second phase is bandwidth conserving; all motion analysis is done in phase one. The intermediate format resulting from phase one uses less storage than the raw data but more than a fully compressed image sequence. It is designed to serve as the source for distribution through diverse channels: transcoding from the intermediate representation to various compressed data streams is both simple and more efficient than single-pass encoding.>